{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<div style=\"text-align: center\"><font color='#dc2624' face='微软雅黑' size = \"6\">用户行为分析(Python_PostgreSQL_echarts)</font></div>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\" # 代码块显示所有执行结果"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<div style=\"text-align: left\"><font color='black' face='微软雅黑' size = \"6\">引言</font><a name='top'></a></div>**\n",
    "\n",
    "本文采用PostgreSQL处理上亿行用户行为数据，通过视图和函数读取数据，使用python和pyecharts进行可视化。\n",
    "基于AARRR模型对用户行为和商品进行分析。与网上的其它教程相比：\n",
    "* 指标计算更加准确，以留存指标计算为例，是以3天前的新用户数量与偏移到3天后的用户数量对比。\n",
    "* 网上也没有基于PostgreSQL数据库进行用户行为分析的教程，其次，大量使用SQL函数读取数据，可以选择性地传入日期范围，方便对比。\n",
    "* 网上也没有基于pyecharts可视化进行用户行为分析的教程。\n",
    "* 总之，就是成熟度非常高，方便直接落地应用。\n",
    "\n",
    "**AARRR模型的5个环节**: <br/>\n",
    "* 第一环节是获取用户（Acquisition）：用户如何找到我们？\n",
    "* 第二环节激活用户（Activation）：用户的首次体验如何？\n",
    "* 第三环节是提高留存率（Retention）：用户会回来吗？\n",
    "* 第四环节是增加收入（Revenue）：如何赚到更多钱？\n",
    "* 第五个环节推荐（Refer）：用户告诉其他人码？\n",
    "* 可以将指标分为五大类：**拉新**指标、**活跃指标**、**留存**指标、**转化**指标、**传播**指标。\n",
    "\n",
    "具体到电商，AARRR模型的指标可以进一步细化为：\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#需要的包\" data-toc-modified-id=\"需要的包-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>需要的包</a></span></li><li><span><a href=\"#数据清洗\" data-toc-modified-id=\"数据清洗-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>数据清洗</a></span><ul class=\"toc-item\"><li><span><a href=\"#读取数据\" data-toc-modified-id=\"读取数据-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>读取数据</a></span></li><li><span><a href=\"#频率统计\" data-toc-modified-id=\"频率统计-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>频率统计</a></span></li><li><span><a href=\"#缺失值统计\" data-toc-modified-id=\"缺失值统计-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>缺失值统计</a></span></li><li><span><a href=\"#时间戳标准化\" data-toc-modified-id=\"时间戳标准化-2.4\"><span class=\"toc-item-num\">2.4&nbsp;&nbsp;</span>时间戳标准化</a></span></li><li><span><a href=\"#提取Date和Hour\" data-toc-modified-id=\"提取Date和Hour-2.5\"><span class=\"toc-item-num\">2.5&nbsp;&nbsp;</span>提取Date和Hour</a></span></li></ul></li><li><span><a href=\"#EDA\" data-toc-modified-id=\"EDA-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>EDA</a></span><ul class=\"toc-item\"><li><span><a href=\"#四种用户行为Daily变化趋势\" data-toc-modified-id=\"四种用户行为Daily变化趋势-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>四种用户行为Daily变化趋势</a></span></li><li><span><a href=\"#四种用户行为Hourly变化趋势\" data-toc-modified-id=\"四种用户行为Hourly变化趋势-3.2\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>四种用户行为Hourly变化趋势</a></span></li></ul></li><li><span><a href=\"#用户行为分析\" data-toc-modified-id=\"用户行为分析-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>用户行为分析</a></span><ul class=\"toc-item\"><li><span><a href=\"#用户流量分析\" data-toc-modified-id=\"用户流量分析-4.1\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span>用户流量分析</a></span><ul class=\"toc-item\"><li><span><a href=\"#PV-and-UV-Metrics-on-Daily\" data-toc-modified-id=\"PV-and-UV-Metrics-on-Daily-4.1.1\"><span class=\"toc-item-num\">4.1.1&nbsp;&nbsp;</span>PV and UV Metrics on Daily</a></span></li><li><span><a href=\"#PV-and-UV-Metrics-on-Hourly\" data-toc-modified-id=\"PV-and-UV-Metrics-on-Hourly-4.1.2\"><span class=\"toc-item-num\">4.1.2&nbsp;&nbsp;</span>PV and UV Metrics on Hourly</a></span></li><li><span><a href=\"#跳失率\" data-toc-modified-id=\"跳失率-4.1.3\"><span class=\"toc-item-num\">4.1.3&nbsp;&nbsp;</span>跳失率</a></span></li></ul></li><li><span><a href=\"#用户购买行为分析\" data-toc-modified-id=\"用户购买行为分析-4.2\"><span class=\"toc-item-num\">4.2&nbsp;&nbsp;</span>用户购买行为分析</a></span><ul class=\"toc-item\"><li><span><a href=\"#Access-People-on-Daily\" data-toc-modified-id=\"Access-People-on-Daily-4.2.1\"><span class=\"toc-item-num\">4.2.1&nbsp;&nbsp;</span>Access People on Daily</a></span></li><li><span><a href=\"#Access-People-on-Hourly\" data-toc-modified-id=\"Access-People-on-Hourly-4.2.2\"><span class=\"toc-item-num\">4.2.2&nbsp;&nbsp;</span>Access People on Hourly</a></span></li><li><span><a href=\"#店铺复购率\" data-toc-modified-id=\"店铺复购率-4.2.3\"><span class=\"toc-item-num\">4.2.3&nbsp;&nbsp;</span>店铺复购率</a></span></li></ul></li><li><span><a href=\"#漏斗模型\" data-toc-modified-id=\"漏斗模型-4.3\"><span class=\"toc-item-num\">4.3&nbsp;&nbsp;</span>漏斗模型</a></span><ul class=\"toc-item\"><li><span><a href=\"#用户行为计数\" data-toc-modified-id=\"用户行为计数-4.3.1\"><span class=\"toc-item-num\">4.3.1&nbsp;&nbsp;</span>用户行为计数</a></span></li><li><span><a href=\"#用户各行为发生总数\" data-toc-modified-id=\"用户各行为发生总数-4.3.2\"><span class=\"toc-item-num\">4.3.2&nbsp;&nbsp;</span>用户各行为发生总数</a></span></li><li><span><a href=\"#用户各行为的转化率\" data-toc-modified-id=\"用户各行为的转化率-4.3.3\"><span class=\"toc-item-num\">4.3.3&nbsp;&nbsp;</span>用户各行为的转化率</a></span></li></ul></li><li><span><a href=\"#用户留存分析\" data-toc-modified-id=\"用户留存分析-4.4\"><span class=\"toc-item-num\">4.4&nbsp;&nbsp;</span>用户留存分析</a></span></li></ul></li><li><span><a href=\"#商品数据分析\" data-toc-modified-id=\"商品数据分析-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>商品数据分析</a></span><ul class=\"toc-item\"><li><span><a href=\"#各商品对应的四种用户行为\" data-toc-modified-id=\"各商品对应的四种用户行为-5.1\"><span class=\"toc-item-num\">5.1&nbsp;&nbsp;</span>各商品对应的四种用户行为</a></span></li><li><span><a href=\"#商品复购率\" data-toc-modified-id=\"商品复购率-5.2\"><span class=\"toc-item-num\">5.2&nbsp;&nbsp;</span>商品复购率</a></span></li></ul></li><li><span><a href=\"#总结\" data-toc-modified-id=\"总结-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>总结</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 需要的包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据处理\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import dask.dataframe as dd\n",
    "from sqlalchemy import create_engine # 数据库\n",
    "\n",
    "# 可视化\n",
    "from palettable.colorbrewer.colorbrewer import get_map\n",
    "import pyecharts.options as opts\n",
    "from pyecharts.charts import Line, Grid, Boxplot, Funnel\n",
    "from pyecharts.globals import ThemeType\n",
    "from pyecharts.commons.utils import JsCode\n",
    "\n",
    "#import matplotlib.pyplot as plt\n",
    "#import matplotlib.cm as cm\n",
    "#from matplotlib.ticker import FuncFormatter # FuncFormatter('.2e')\n",
    "#from matplotlib.ticker import PercentFormatter # PercentFormatter(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据清洗"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据\n",
    "数据来源: https://tianchi.aliyun.com/dataset/dataDetail?dataId=649  \n",
    "因为数据量太大，所以使用`dask`库读取数据，设置好读取参数后。`dask`采用的是分区懒加载模式，并不会全部加载到内存。\n",
    "然后将数据写入pg数据库。上亿条记录，MySQL吃不消。为了提高速度，在`postgresql.conf`配置脚本中设置`max_worker_processes = 6`表示6核并行。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1.67 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>category_id</th>\n",
       "      <th>behavior_type</th>\n",
       "      <th>event_tm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2268318</td>\n",
       "      <td>2520377</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511544070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2333346</td>\n",
       "      <td>2520771</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511561733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2576651</td>\n",
       "      <td>149192</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511572885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3830808</td>\n",
       "      <td>4181361</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511593493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4365585</td>\n",
       "      <td>2520377</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511596146</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  item_id  category_id behavior_type    event_tm\n",
       "0        1  2268318      2520377            pv  1511544070\n",
       "1        1  2333346      2520771            pv  1511561733\n",
       "2        1  2576651       149192            pv  1511572885\n",
       "3        1  3830808      4181361            pv  1511593493\n",
       "4        1  4365585      2520377            pv  1511596146"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time \n",
    "df1 = dd.read_csv('F:/py_input_output/data/UserBehavior.csv', \n",
    "                  blocksize=100e6, # 分区最多100Mb\n",
    "                  header=None, \n",
    "                  names=['user_id', 'item_id', 'category_id', 'behavior_type', 'event_tm'])\n",
    "\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id           int64\n",
       "item_id           int64\n",
       "category_id       int64\n",
       "behavior_type    object\n",
       "event_tm          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn_dict = {\"driver\": \"postgresql+psycopg2\",\n",
    "             \"username\": \"postgres\",  # \"jihuabu\"\n",
    "             \"password\": \"postj1huabu\",\n",
    "             \"host\": \"localhost\", # \"192.168.8.12\"\n",
    "             \"database\": \"db_test\"}\n",
    "uri = str.format(\"{}://{}:{}@{}/{}\",\n",
    "                 conn_dict['driver'],\n",
    "                 conn_dict['username'],\n",
    "                 conn_dict['password'],\n",
    "                 conn_dict['host'],\n",
    "                 conn_dict['database'])\n",
    "engine = create_engine(uri)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```sql\n",
    "create table user_behavior\n",
    "(\n",
    "    user_id int,\n",
    "    item_id int,\n",
    "    category_id int,\n",
    "    behavior_type varchar(10),\n",
    "    event_tm int\n",
    ");\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "%%time\n",
    "df1.to_sql('user_behavior', uri=uri, index=False, if_exists='append')\n",
    "print(\"写入数据库完成!\") # 共花费28min\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 频率统计"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "%%time\n",
    "pd.read_sql_query(\"\"\"\n",
    "select\n",
    "    count(distinct user_id) as user_id,\n",
    "    count(distinct item_id) as item_id,\n",
    "    count(distinct category_id) as category_id,\n",
    "    count(distinct behavior_type) as behavior_type\n",
    "from user_behavior;\n",
    "\"\"\", con=engine)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "%%time\n",
    "pd.read_sql_query(\"\"\"\n",
    "select\n",
    "    behavior_type,\n",
    "    freq,\n",
    "    round(cast(freq as numeric) / cast((select count(*) from user_behavior) as numeric)*100, 2) as perct\n",
    "from\n",
    "(\n",
    "    select\n",
    "        behavior_type,\n",
    "        count(behavior_type) as freq\n",
    "    from user_behavior\n",
    "    group by behavior_type\n",
    ") ug;\n",
    "\"\"\", con=engine)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注释：**  \n",
    "* **pv**, Page view of an item's detail page, 相当于点击item。\n",
    "* **buy**, 购买item。\n",
    "* **cart**, 添加item到购物车。\n",
    "* **fav**, 喜欢item, 相当于收藏。\n",
    "\n",
    "结果表明，约90%记录都属于浏览商品，而不是购买"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 缺失值统计"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "%%time\n",
    "pd.read_sql_query(\"\"\"\n",
    "select\n",
    "    sum(case when user_id is null then 1 else 0 end) as user_id,\n",
    "    sum(case when item_id is null then 1 else 0 end) as item_id,\n",
    "    sum(case when category_id is null then 1 else 0 end) as category_id,\n",
    "    sum(case when behavior_type is null then 1 else 0 end) as behavior_type,\n",
    "    sum(case when event_tm is null then 1 else 0 end) as event_tm\n",
    "from user_behavior;\n",
    "\"\"\", con=engine)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结果表明数据集没有缺失值。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 时间戳标准化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "engine.execute(\"set timezone = 'PRC'\") # 设置东八区时区\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "%%time\n",
    "pd.read_sql_query(\"\"\"\n",
    "select\n",
    "   'min' as \"分位数\",\n",
    "   min(event_tm) as \"值\"\n",
    "from user_behavior\n",
    "union all\n",
    "select\n",
    "   '0.25' as \"分位数\",\n",
    "   percentile_disc(0.25) within group (order by u.event_tm) as \"值\"\n",
    "from user_behavior u\n",
    "union all\n",
    "select\n",
    "   '0.5' as \"分位数\",\n",
    "   percentile_disc(0.5) within group (order by u.event_tm) as \"值\"\n",
    "from user_behavior u\n",
    "union all\n",
    "select\n",
    "   '0.75' as \"分位数\",\n",
    "   percentile_disc(0.75) within group (order by u.event_tm) as \"值\"\n",
    "from user_behavior u\n",
    "union all\n",
    "select\n",
    "   'max' as \"分位数\",\n",
    "   max(event_tm) as \"值\"\n",
    "from user_behavior;\n",
    "\"\"\", con=engine)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "engine.execute(\"alter table user_behavior add column event_datetime timestamp;\")\n",
    "engine.execute(\"update user_behavior set event_datetime = to_timestamp(event_tm);\")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "%%time\n",
    "pd.read_sql_query(\"\"\"\n",
    "select\n",
    "   'min' as \"分位数\",\n",
    "   min(event_datetime) as \"值\"\n",
    "from user_behavior\n",
    "union all\n",
    "select\n",
    "   '0.25' as \"分位数\",\n",
    "   percentile_disc(0.25) within group (order by u.event_datetime) as \"值\"\n",
    "from user_behavior u\n",
    "union all\n",
    "select\n",
    "   '0.5' as \"分位数\",\n",
    "   percentile_disc(0.5) within group (order by u.event_datetime) as \"值\"\n",
    "from user_behavior u\n",
    "union all\n",
    "select\n",
    "   '0.75' as \"分位数\",\n",
    "   percentile_disc(0.75) within group (order by u.event_datetime) as \"值\"\n",
    "from user_behavior u\n",
    "union all\n",
    "select\n",
    "   'max' as \"分位数\",\n",
    "   max(event_datetime) as \"值\"\n",
    "from user_behavior;\n",
    "\"\"\", con=engine)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从[文档](https://tianchi.aliyun.com/dataset/dataDetail?dataId=649)来看，数据记录时间为2017年11月25到12月3号，因此这个1902年和2037年应该属于缺失值。遂删去"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "%%time\n",
    "engine.execute(\"\"\"\n",
    "delete\n",
    "from user_behavior\n",
    "where event_datetime not between timestamp '2017-11-25' and timestamp '2017-12-03';\n",
    "\"\"\")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86433301"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engine.execute(\"select count(*) from user_behavior\").fetchone()[0] # 还剩余8千多万行数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 提取Date和Hour\n",
    "首先从时间戳中提取日期和小时数据。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "%%time\n",
    "engine.execute(\"alter table user_behavior add column event_date date;\")\n",
    "engine.execute(\"alter table user_behavior add column event_hour int;\")\n",
    "engine.execute(\"update user_behavior set event_date = date(event_datetime);\")\n",
    "engine.execute(\"update user_behavior set event_hour = date_part('hour', event_datetime);\")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 四种用户行为Daily变化趋势"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 5.15 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日期</th>\n",
       "      <th>pv</th>\n",
       "      <th>fac</th>\n",
       "      <th>cart</th>\n",
       "      <th>buy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>9353423</td>\n",
       "      <td>302071</td>\n",
       "      <td>563376</td>\n",
       "      <td>201145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-11-26</td>\n",
       "      <td>9567423</td>\n",
       "      <td>308954</td>\n",
       "      <td>582581</td>\n",
       "      <td>205644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-11-27</td>\n",
       "      <td>9041187</td>\n",
       "      <td>291221</td>\n",
       "      <td>541904</td>\n",
       "      <td>226835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-11-28</td>\n",
       "      <td>8842933</td>\n",
       "      <td>289100</td>\n",
       "      <td>534157</td>\n",
       "      <td>212000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>9210821</td>\n",
       "      <td>298587</td>\n",
       "      <td>551593</td>\n",
       "      <td>223072</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          日期       pv     fac    cart     buy\n",
       "0 2017-11-25  9353423  302071  563376  201145\n",
       "1 2017-11-26  9567423  308954  582581  205644\n",
       "2 2017-11-27  9041187  291221  541904  226835\n",
       "3 2017-11-28  8842933  289100  534157  212000\n",
       "4 2017-11-29  9210821  298587  551593  223072"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "count_daily = pd.read_sql_query(\"\"\"\n",
    "select\n",
    "    event_date as \"日期\",\n",
    "    sum(case when behavior_type = 'pv' then 1 else 0 end) as \"pv\",\n",
    "    sum(case when behavior_type = 'fav' then 1 else 0 end) as \"fac\",\n",
    "    sum(case when behavior_type = 'cart' then 1 else 0 end) as \"cart\",\n",
    "    sum(case when behavior_type = 'buy' then 1 else 0 end) as \"buy\"\n",
    "from user_behavior\n",
    "group by event_date\n",
    "order by event_date;\n",
    "\"\"\", con=engine, parse_dates=['日期'])\n",
    "count_daily.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x23a17995fc8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
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    }
   ],
   "source": [
    "(\n",
    "    Line(init_opts=opts.InitOpts(theme=ThemeType.CHALK))\n",
    "    .add_xaxis(count_daily['日期'].dt.strftime('%Y-%m-%d').to_list())\n",
    "    .add_yaxis(\"pv\", count_daily['pv'].to_list()) \n",
    "    .add_yaxis(\"fac\", count_daily['fac'].to_list()) \n",
    "    .add_yaxis(\"cart\", count_daily['cart'].to_list()) \n",
    "    .add_yaxis(\"buy\", count_daily['buy'].to_list())\n",
    "    .set_series_opts(\n",
    "        label_opts=opts.LabelOpts(\n",
    "            formatter=JsCode(\"function(x){return x.data[1].toExponential(2);}\"), # Labels格式\n",
    "        ),\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"每日用户行为计数\"),\n",
    "        yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter=JsCode(\"function(x){return x.toExponential(2)}\"))),\n",
    "        toolbox_opts=opts.ToolboxOpts(), # 工具箱\n",
    "        datazoom_opts=[opts.DataZoomOpts(is_realtime=True, range_start=0, range_end=100), \n",
    "                       opts.DataZoomOpts(orient=\"vertical\", range_start=0, range_end=100)], # 同时添加水平和垂直滑动条\n",
    "    )\n",
    "    .render_notebook()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Timestamp('2017-12-02').weekday() # 周六"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 从图中可以看到，每天pv的次数最多，是第2名cart的一个数量级以上，再其次是fav, 最后才是buy。\n",
    "* 时间上，星期六达到峰值。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 四种用户行为Hourly变化趋势"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 5.3 s\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>时</th>\n",
       "      <th>pv</th>\n",
       "      <th>fac</th>\n",
       "      <th>cart</th>\n",
       "      <th>buy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2563909</td>\n",
       "      <td>88181</td>\n",
       "      <td>150993</td>\n",
       "      <td>49880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1192812</td>\n",
       "      <td>42968</td>\n",
       "      <td>71283</td>\n",
       "      <td>19867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>650233</td>\n",
       "      <td>23329</td>\n",
       "      <td>38913</td>\n",
       "      <td>10346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>446728</td>\n",
       "      <td>15628</td>\n",
       "      <td>27514</td>\n",
       "      <td>6832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>387004</td>\n",
       "      <td>12724</td>\n",
       "      <td>24507</td>\n",
       "      <td>5853</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   时       pv    fac    cart    buy\n",
       "0  0  2563909  88181  150993  49880\n",
       "1  1  1192812  42968   71283  19867\n",
       "2  2   650233  23329   38913  10346\n",
       "3  3   446728  15628   27514   6832\n",
       "4  4   387004  12724   24507   5853"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "count_hourly = pd.read_sql_query(\"\"\"\n",
    "select\n",
    "    event_hour as \"时\",\n",
    "    sum(case when behavior_type = 'pv' then 1 else 0 end) as \"pv\",\n",
    "    sum(case when behavior_type = 'fav' then 1 else 0 end) as \"fac\",\n",
    "    sum(case when behavior_type = 'cart' then 1 else 0 end) as \"cart\",\n",
    "    sum(case when behavior_type = 'buy' then 1 else 0 end) as \"buy\"\n",
    "from user_behavior\n",
    "group by event_hour\n",
    "order by event_hour;\n",
    "\"\"\", con=engine)\n",
    "count_hourly.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "        <div id=\"e7d3233b72634affb17afc172e603d2a\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts', 'chalk'], function(echarts) {\n",
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       "            ],\n",
       "            \"hoverAnimation\": true,\n",
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       "            \"lineStyle\": {\n",
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       "    ],\n",
       "    \"legend\": [\n",
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       "            ],\n",
       "            \"selected\": {\n",
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       "                \"fac\": true,\n",
       "                \"cart\": true,\n",
       "                \"buy\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
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       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
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       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"xAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            },\n",
       "            \"data\": [\n",
       "                0,\n",
       "                1,\n",
       "                2,\n",
       "                3,\n",
       "                4,\n",
       "                5,\n",
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       "                8,\n",
       "                9,\n",
       "                10,\n",
       "                11,\n",
       "                12,\n",
       "                13,\n",
       "                14,\n",
       "                15,\n",
       "                16,\n",
       "                17,\n",
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       "                19,\n",
       "                20,\n",
       "                21,\n",
       "                22,\n",
       "                23\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"axisLabel\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8,\n",
       "                \"formatter\": function(x){return x.toExponential(2)}\n",
       "            },\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
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       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
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       "        {\n",
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       "                \"show\": true,\n",
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       "                \"pixelRatio\": 1\n",
       "            },\n",
       "            \"restore\": {\n",
       "                \"show\": true,\n",
       "                \"title\": \"\\u8fd8\\u539f\"\n",
       "            },\n",
       "            \"dataView\": {\n",
       "                \"show\": true,\n",
       "                \"title\": \"\\u6570\\u636e\\u89c6\\u56fe\",\n",
       "                \"readOnly\": false,\n",
       "                \"lang\": [\n",
       "                    \"\\u6570\\u636e\\u89c6\\u56fe\",\n",
       "                    \"\\u5173\\u95ed\",\n",
       "                    \"\\u5237\\u65b0\"\n",
       "                ],\n",
       "                \"backgroundColor\": \"#fff\",\n",
       "                \"textareaColor\": \"#fff\",\n",
       "                \"textareaBorderColor\": \"#333\",\n",
       "                \"textColor\": \"#000\",\n",
       "                \"buttonColor\": \"#c23531\",\n",
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       "            },\n",
       "            \"dataZoom\": {\n",
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       "                    \"zoom\": \"\\u533a\\u57df\\u7f29\\u653e\",\n",
       "                    \"back\": \"\\u533a\\u57df\\u7f29\\u653e\\u8fd8\\u539f\"\n",
       "                },\n",
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       "                    \"line\",\n",
       "                    \"bar\",\n",
       "                    \"stack\",\n",
       "                    \"tiled\"\n",
       "                ],\n",
       "                \"title\": {\n",
       "                    \"line\": \"\\u5207\\u6362\\u4e3a\\u6298\\u7ebf\\u56fe\",\n",
       "                    \"bar\": \"\\u5207\\u6362\\u4e3a\\u67f1\\u72b6\\u56fe\",\n",
       "                    \"stack\": \"\\u5207\\u6362\\u4e3a\\u5806\\u53e0\",\n",
       "                    \"tiled\": \"\\u5207\\u6362\\u4e3a\\u5e73\\u94fa\"\n",
       "                },\n",
       "                \"icon\": {}\n",
       "            },\n",
       "            \"brush\": {\n",
       "                \"icon\": {},\n",
       "                \"title\": {\n",
       "                    \"rect\": \"\\u77e9\\u5f62\\u9009\\u62e9\",\n",
       "                    \"polygon\": \"\\u5708\\u9009\",\n",
       "                    \"lineX\": \"\\u6a2a\\u5411\\u9009\\u62e9\",\n",
       "                    \"lineY\": \"\\u7eb5\\u5411\\u9009\\u62e9\",\n",
       "                    \"keep\": \"\\u4fdd\\u6301\\u9009\\u62e9\",\n",
       "                    \"clear\": \"\\u6e05\\u9664\\u9009\\u62e9\"\n",
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       "    },\n",
       "    \"dataZoom\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"type\": \"slider\",\n",
       "            \"realtime\": true,\n",
       "            \"start\": 0,\n",
       "            \"end\": 100,\n",
       "            \"orient\": \"horizontal\",\n",
       "            \"zoomLock\": false,\n",
       "            \"filterMode\": \"filter\"\n",
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       "        {\n",
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       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x23a17e00888>"
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     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    Line(init_opts=opts.InitOpts(theme=ThemeType.CHALK))\n",
    "    .add_xaxis(count_hourly['时'].to_list())\n",
    "    .add_yaxis(\"pv\", count_hourly['pv'].to_list()) \n",
    "    .add_yaxis(\"fac\", count_hourly['fac'].to_list()) \n",
    "    .add_yaxis(\"cart\", count_hourly['cart'].to_list()) \n",
    "    .add_yaxis(\"buy\", count_hourly['buy'].to_list())\n",
    "    .set_series_opts(\n",
    "        label_opts=opts.LabelOpts(\n",
    "            formatter=JsCode(\"function(x){return x.data[1].toExponential(2);}\"), # Labels格式\n",
    "        )\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"每时用户行为计数\"),\n",
    "        yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter=JsCode(\"function(x){return x.toExponential(2)}\"))),\n",
    "        toolbox_opts=opts.ToolboxOpts(), # 工具箱\n",
    "        datazoom_opts=[opts.DataZoomOpts(is_realtime=True, range_start=0, range_end=100), \n",
    "                       opts.DataZoomOpts(orient=\"vertical\", range_start=0, range_end=100)], # 同时添加水平和垂直滑动条\n",
    "    )\n",
    "    .render_notebook()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 从图中可以看到，从早上5点到上午10点，用户行为处于爬升期，白天10点到18点，属于上班时间，用户行为处于平台期，18点下班后，用户行为陡增，晚上8点后，用户行为开始下降。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 用户行为分析\n",
    "在数据分析前明确分析目标:\n",
    "* **了解用户活跃时间段**：发现用户活跃的日期，及每天活跃的时间段\n",
    "* **提升转化率**：从点击→收藏→添加→购买，各个环节的转化率是怎样的？哪个环节可以提升？\n",
    "* **优化产品类目**：哪些产品受欢迎，不受欢迎\n",
    "* **找出核心用户**：找出购买次数多的用户群体"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 用户流量分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PV and UV Metrics on Daily\n",
    "‘PV’表示访问量，‘UV’表示人数, ‘UPV’表示平均每人访问量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 55s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日期</th>\n",
       "      <th>UV</th>\n",
       "      <th>PV</th>\n",
       "      <th>UPV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>706641</td>\n",
       "      <td>9353423.0</td>\n",
       "      <td>13.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-11-26</td>\n",
       "      <td>715516</td>\n",
       "      <td>9567423.0</td>\n",
       "      <td>13.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-11-27</td>\n",
       "      <td>710094</td>\n",
       "      <td>9041187.0</td>\n",
       "      <td>13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-11-28</td>\n",
       "      <td>709257</td>\n",
       "      <td>8842933.0</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>718922</td>\n",
       "      <td>9210821.0</td>\n",
       "      <td>13.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          日期      UV         PV   UPV\n",
       "0 2017-11-25  706641  9353423.0  13.6\n",
       "1 2017-11-26  715516  9567423.0  13.7\n",
       "2 2017-11-27  710094  9041187.0  13.1\n",
       "3 2017-11-28  709257  8842933.0  12.9\n",
       "4 2017-11-29  718922  9210821.0  13.2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "pv_uv_daily = pd.read_sql_query(\"\"\"\n",
    "select\n",
    "    t1.event_date as \"日期\",\n",
    "    t1.uv as \"UV\",\n",
    "    t3.pv as \"PV\",\n",
    "    t3.upv as \"UPV\"\n",
    "from\n",
    "(\n",
    "    select\n",
    "        event_date,\n",
    "        count(distinct user_id) as uv\n",
    "    from user_behavior\n",
    "    group by event_date\n",
    ") t1\n",
    "left join\n",
    "(\n",
    "    select\n",
    "        t2.event_date,\n",
    "        sum(t2.upv) as pv,\n",
    "        round(avg(cast(t2.upv as numeric)), 1) as upv\n",
    "    from\n",
    "    (\n",
    "        select\n",
    "            event_date,\n",
    "            count(user_id) as upv\n",
    "        from user_behavior\n",
    "        where behavior_type = 'pv'\n",
    "        group by event_date, user_id\n",
    "    ) t2\n",
    "    group by event_date\n",
    ") t3\n",
    "    on t1.event_date = t3.event_date\n",
    "order by t1.event_date;\n",
    "\"\"\", con=engine, parse_dates=['日期'])\n",
    "pv_uv_daily.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "<script>\n",
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       "</script>\n",
       "\n",
       "        <div id=\"2440487c3697492e93c9d4fab7981385\" style=\"width:850px; height:800px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_2440487c3697492e93c9d4fab7981385 = echarts.init(\n",
       "                    document.getElementById('2440487c3697492e93c9d4fab7981385'), 'white', {renderer: 'canvas'});\n",
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       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"series\": [\n",
       "        {\n",
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       "            \"name\": \"PV\",\n",
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       "            \"xAxisIndex\": 0,\n",
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       "                    \"2017-12-02\",\n",
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       "                ],\n",
       "                [\n",
       "                    \"2017-12-03\",\n",
       "                    202.0\n",
       "                ]\n",
       "            ],\n",
       "            \"hoverAnimation\": true,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8,\n",
       "                \"formatter\": function(x){return x.data[1].toExponential(2);}\n",
       "            },\n",
       "            \"lineStyle\": {\n",
       "                \"show\": true,\n",
       "                \"width\": 3,\n",
       "                \"opacity\": 1,\n",
       "                \"curveness\": 0,\n",
       "                \"type\": \"solid\"\n",
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       "            \"areaStyle\": {\n",
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       "                \"period\": 4\n",
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       "            \"name\": \"UV\",\n",
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       "            \"xAxisIndex\": 1,\n",
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       "                    715516\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-11-27\",\n",
       "                    710094\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-11-28\",\n",
       "                    709257\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-11-29\",\n",
       "                    718922\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-11-30\",\n",
       "                    730597\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-12-01\",\n",
       "                    740139\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-12-02\",\n",
       "                    970401\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-12-03\",\n",
       "                    229\n",
       "                ]\n",
       "            ],\n",
       "            \"hoverAnimation\": true,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8,\n",
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       "        {\n",
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       "            \"name\": \"UPV\",\n",
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       "                ],\n",
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       "                    13.7\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-11-27\",\n",
       "                    13.1\n",
       "                ],\n",
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       "                    \"2017-11-28\",\n",
       "                    12.9\n",
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       "                ],\n",
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       "                    \"2017-11-30\",\n",
       "                    13.2\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-12-01\",\n",
       "                    13.5\n",
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       "                    \"2017-12-02\",\n",
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      "text/plain": [
       "<pyecharts.render.display.HTML at 0x23a173147c8>"
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     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line_pv_daily = (\n",
    "    Line(init_opts=opts.InitOpts(theme=ThemeType.CHALK))\n",
    "    .add_xaxis(pv_uv_daily['日期'].dt.strftime('%Y-%m-%d').to_list())\n",
    "    .add_yaxis('PV', pv_uv_daily['PV'].to_list(), \n",
    "              linestyle_opts=opts.LineStyleOpts(width=3), # 设置线宽、颜色、线型\n",
    "              )\n",
    "    .set_series_opts(\n",
    "        label_opts=opts.LabelOpts(\n",
    "            formatter=JsCode(\"function(x){return x.data[1].toExponential(2);}\"), # Labels格式\n",
    "        )\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"PV and UV Metrics on Daily\", pos_left=\"center\"),\n",
    "        yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter=JsCode(\"function(x){return x.toExponential(2)}\"))),\n",
    "        xaxis_opts=opts.AxisOpts(axisline_opts=opts.AxisLineOpts(is_on_zero=True)),\n",
    "        toolbox_opts=opts.ToolboxOpts(), # 工具箱\n",
    "        legend_opts=opts.LegendOpts(pos_left=\"7%\"),\n",
    "        datazoom_opts=[opts.DataZoomOpts(is_realtime=True, #type_='inside', \n",
    "                                         start_value=pv_uv_daily['日期'].min().strftime('%Y-%m-%d'), \n",
    "                                         end_value=pv_uv_daily['日期'].max().strftime('%Y-%m-%d'), \n",
    "                                         xaxis_index=[0, 1])],\n",
    "        \n",
    "    )\n",
    ")\n",
    "line_uv_daily = (\n",
    "    Line(init_opts=opts.InitOpts(theme=ThemeType.CHALK))\n",
    "    .add_xaxis(pv_uv_daily['日期'].dt.strftime('%Y-%m-%d').to_list())\n",
    "    .add_yaxis('UV', pv_uv_daily['UV'].to_list(), \n",
    "              linestyle_opts=opts.LineStyleOpts(width=3), # 设置线宽、颜色、线型\n",
    "              )\n",
    "    .set_series_opts(\n",
    "        label_opts=opts.LabelOpts(\n",
    "            formatter=JsCode(\"function(x){return x.data[1].toExponential(2);}\"), # Labels格式\n",
    "        )\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter=JsCode(\"function(x){return x.toExponential(2)}\"))),\n",
    "        xaxis_opts=opts.AxisOpts(axisline_opts=opts.AxisLineOpts(is_on_zero=True)),\n",
    "        legend_opts=opts.LegendOpts(pos_left=\"14%\"),\n",
    "        datazoom_opts=[opts.DataZoomOpts(is_realtime=True, #type_='inside', \n",
    "                                         start_value=pv_uv_daily['日期'].min().strftime('%Y-%m-%d'), \n",
    "                                         end_value=pv_uv_daily['日期'].max().strftime('%Y-%m-%d'), \n",
    "                                         xaxis_index=[0, 1])],\n",
    "    )\n",
    ")\n",
    "line_upv_daily = (\n",
    "    Line(init_opts=opts.InitOpts(theme=ThemeType.CHALK))\n",
    "    .add_xaxis(pv_uv_daily['日期'].dt.strftime('%Y-%m-%d').to_list())\n",
    "    .add_yaxis('UPV', pv_uv_daily['UPV'].to_list(), \n",
    "              linestyle_opts=opts.LineStyleOpts(width=3), # 设置线宽、颜色、线型\n",
    "              )\n",
    "    .set_global_opts(\n",
    "        xaxis_opts=opts.AxisOpts(axisline_opts=opts.AxisLineOpts(is_on_zero=True)),\n",
    "        legend_opts=opts.LegendOpts(pos_left=\"21%\"),\n",
    "        datazoom_opts=[opts.DataZoomOpts(is_realtime=True, #type_='inside', \n",
    "                                         start_value=pv_uv_daily['日期'].min().strftime('%Y-%m-%d'), \n",
    "                                         end_value=pv_uv_daily['日期'].max().strftime('%Y-%m-%d'), \n",
    "                                         xaxis_index=[0, 1])],\n",
    "    )\n",
    ")\n",
    "\n",
    "(\n",
    "    Grid(init_opts=opts.InitOpts(width=\"850px\", height=\"800px\"))\n",
    "    .add(chart=line_pv_daily, grid_opts=opts.GridOpts(pos_left=80, pos_right=10, pos_top=\"5%\", height=\"28%\"))\n",
    "    .add(chart=line_uv_daily, grid_opts=opts.GridOpts(pos_left=80, pos_right=10, pos_top=\"35%\", height=\"28%\"))\n",
    "    .add(chart=line_upv_daily, grid_opts=opts.GridOpts(pos_left=80, pos_right=10, pos_top=\"65%\", height=\"23%\"))\n",
    "    .render_notebook()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PV and UV Metrics on Hourly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 14s\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>时</th>\n",
       "      <th>UV</th>\n",
       "      <th>PV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>281954</td>\n",
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       "      <td>1192812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>85812</td>\n",
       "      <td>650233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>62592</td>\n",
       "      <td>446728</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>56490</td>\n",
       "      <td>387004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   时      UV       PV\n",
       "0  0  281954  2563909\n",
       "1  1  146207  1192812\n",
       "2  2   85812   650233\n",
       "3  3   62592   446728\n",
       "4  4   56490   387004"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "pv_uv_hourly = pd.read_sql_query(\"\"\"\n",
    "select\n",
    "    t1.event_hour as \"时\",\n",
    "    t1.uv as \"UV\",\n",
    "    t2.pv as \"PV\"\n",
    "from\n",
    "(\n",
    "    select\n",
    "        event_hour,\n",
    "        count(distinct user_id) as uv\n",
    "    from user_behavior\n",
    "    group by event_hour\n",
    ") t1\n",
    "left join\n",
    "(\n",
    "    select\n",
    "        event_hour,\n",
    "        count(user_id) as pv\n",
    "    from user_behavior\n",
    "    where behavior_type = 'pv'\n",
    "    group by event_hour\n",
    ") t2\n",
    "    on t1.event_hour = t2.event_hour\n",
    "order by t1.event_hour;\n",
    "\"\"\", con=engine, parse_dates=['日期'])\n",
    "pv_uv_hourly.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    require.config({\n",
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       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"2ff1d3a918b7459f874819666de48fb2\" style=\"width:850px; height:700px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_2ff1d3a918b7459f874819666de48fb2 = echarts.init(\n",
       "                    document.getElementById('2ff1d3a918b7459f874819666de48fb2'), 'white', {renderer: 'canvas'});\n",
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       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"line\",\n",
       "            \"name\": \"PV\",\n",
       "            \"connectNulls\": false,\n",
       "            \"xAxisIndex\": 0,\n",
       "            \"yAxisIndex\": 0,\n",
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       "            \"showSymbol\": true,\n",
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       "                ],\n",
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       "                ],\n",
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       "                ],\n",
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       "                ],\n",
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       "                ],\n",
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       "                ],\n",
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       "                    17,\n",
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       "                ],\n",
       "                [\n",
       "                    21,\n",
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       "                ],\n",
       "                [\n",
       "                    22,\n",
       "                    6455469\n",
       "                ],\n",
       "                [\n",
       "                    23,\n",
       "                    4885043\n",
       "                ]\n",
       "            ],\n",
       "            \"hoverAnimation\": true,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8,\n",
       "                \"formatter\": function(x){return x.data[1].toExponential(2);}\n",
       "            },\n",
       "            \"lineStyle\": {\n",
       "                \"show\": true,\n",
       "                \"width\": 3,\n",
       "                \"opacity\": 1,\n",
       "                \"curveness\": 0,\n",
       "                \"type\": \"solid\"\n",
       "            },\n",
       "            \"areaStyle\": {\n",
       "                \"opacity\": 0\n",
       "            },\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 0,\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        },\n",
       "        {\n",
       "            \"type\": \"line\",\n",
       "            \"name\": \"UV\",\n",
       "            \"connectNulls\": false,\n",
       "            \"xAxisIndex\": 1,\n",
       "            \"yAxisIndex\": 1,\n",
       "            \"symbolSize\": 4,\n",
       "            \"showSymbol\": true,\n",
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       "            \"step\": false,\n",
       "            \"data\": [\n",
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       "                    8,\n",
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       "                \"opacity\": 1,\n",
       "                \"curveness\": 0,\n",
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       "                \"brushType\": \"stroke\",\n",
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       "        {\n",
       "            \"data\": [\n",
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       "            \"selected\": {\n",
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       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
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       "        \"showContent\": true,\n",
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       "        \"showDelay\": 0,\n",
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       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"axisLine\": {\n",
       "                \"show\": true,\n",
       "                \"onZero\": true,\n",
       "                \"onZeroAxisIndex\": 0\n",
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       "            \"inverse\": false,\n",
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   ],
   "source": [
    "line_pv_hourly = (\n",
    "    Line(init_opts=opts.InitOpts(theme=ThemeType.CHALK))\n",
    "    .add_xaxis(pv_uv_hourly['时'].to_list())\n",
    "    .add_yaxis('PV', pv_uv_hourly['PV'].to_list(), \n",
    "              linestyle_opts=opts.LineStyleOpts(width=3), # 设置线宽、颜色、线型\n",
    "              )\n",
    "    .set_series_opts(\n",
    "        label_opts=opts.LabelOpts(\n",
    "            formatter=JsCode(\"function(x){return x.data[1].toExponential(2);}\"), # Labels格式\n",
    "        )\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"PV and UV Metrics on Hourly\", pos_left=\"center\"),\n",
    "        yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter=JsCode(\"function(x){return x.toExponential(2)}\"))),\n",
    "        xaxis_opts=opts.AxisOpts(axisline_opts=opts.AxisLineOpts(is_on_zero=True)),\n",
    "        toolbox_opts=opts.ToolboxOpts(), # 工具箱\n",
    "        legend_opts=opts.LegendOpts(pos_left=\"7%\"),\n",
    "        datazoom_opts=[opts.DataZoomOpts(is_realtime=True, #type_='inside', \n",
    "                                         start_value=pv_uv_hourly['时'].min(), \n",
    "                                         end_value=pv_uv_hourly['时'].max(), \n",
    "                                         xaxis_index=[0, 1])],\n",
    "        \n",
    "    )\n",
    ")\n",
    "line_uv_hourly = (\n",
    "    Line(init_opts=opts.InitOpts(theme=ThemeType.CHALK))\n",
    "    .add_xaxis(pv_uv_hourly['时'].to_list())\n",
    "    .add_yaxis('UV', pv_uv_hourly['UV'].to_list(), \n",
    "              linestyle_opts=opts.LineStyleOpts(width=3), # 设置线宽、颜色、线型\n",
    "              )\n",
    "    .set_series_opts(\n",
    "        label_opts=opts.LabelOpts(\n",
    "            formatter=JsCode(\"function(x){return x.data[1].toExponential(2);}\"), # Labels格式\n",
    "        )\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter=JsCode(\"function(x){return x.toExponential(2)}\"))),\n",
    "        xaxis_opts=opts.AxisOpts(axisline_opts=opts.AxisLineOpts(is_on_zero=True)),\n",
    "        legend_opts=opts.LegendOpts(pos_left=\"14%\"),\n",
    "        datazoom_opts=[opts.DataZoomOpts(is_realtime=True, #type_='inside', \n",
    "                                         start_value=pv_uv_hourly['时'].min(), \n",
    "                                         end_value=pv_uv_hourly['时'].max(), \n",
    "                                         xaxis_index=[0, 1])],\n",
    "    )\n",
    ")\n",
    "\n",
    "(\n",
    "    Grid(init_opts=opts.InitOpts(width=\"850px\", height=\"700px\"))\n",
    "    .add(chart=line_pv_hourly, grid_opts=opts.GridOpts(pos_left=80, pos_right=10, pos_top=\"5%\", height=\"40%\"))\n",
    "    .add(chart=line_uv_hourly, grid_opts=opts.GridOpts(pos_left=80, pos_right=10, pos_top=\"50%\", height=\"40%\"))\n",
    "    .render_notebook()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 跳失率\n",
    "跳失率=只有浏览行为的用户数/总用户数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 17.9 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日期</th>\n",
       "      <th>跳失率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>0.4705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-11-26</td>\n",
       "      <td>0.4611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-11-27</td>\n",
       "      <td>0.4622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-11-28</td>\n",
       "      <td>0.4675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>0.4608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          日期     跳失率\n",
       "0 2017-11-25  0.4705\n",
       "1 2017-11-26  0.4611\n",
       "2 2017-11-27  0.4622\n",
       "3 2017-11-28  0.4675\n",
       "4 2017-11-29  0.4608"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "only_pv_df = pd.read_sql_query(\"\"\"\n",
    "select\n",
    "    event_date as \"日期\",\n",
    "    round(\n",
    "        cast(sum(case when (fav_num = 0 and cart_num = 0 and buy_num = 0) then 1 else 0 end) as numeric) /\n",
    "        cast(count(user_id) as numeric)\n",
    "        ,4\n",
    "    ) as \"跳失率\"\n",
    "from\n",
    "(\n",
    "    select\n",
    "        event_date,\n",
    "        user_id,\n",
    "        sum(case when behavior_type = 'fav' then 1 else 0 end) as fav_num,\n",
    "        sum(case when behavior_type = 'cart' then 1 else 0 end) as cart_num,\n",
    "        sum(case when behavior_type = 'buy' then 1 else 0 end) as buy_num\n",
    "    from user_behavior\n",
    "    group by event_date, user_id\n",
    ") ug\n",
    "group by event_date\n",
    "order by event_date;\n",
    "\"\"\", con=engine, parse_dates=['日期'])\n",
    "only_pv_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min', 'chalk':'https://assets.pyecharts.org/assets/themes/chalk'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"57977501ada34d5dbe4a2dced69d2de9\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts', 'chalk'], function(echarts) {\n",
       "                var chart_57977501ada34d5dbe4a2dced69d2de9 = echarts.init(\n",
       "                    document.getElementById('57977501ada34d5dbe4a2dced69d2de9'), 'chalk', {renderer: 'canvas'});\n",
       "                var option_57977501ada34d5dbe4a2dced69d2de9 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"line\",\n",
       "            \"name\": \"\\u8df3\\u5931\\u7387\",\n",
       "            \"connectNulls\": false,\n",
       "            \"symbolSize\": 10,\n",
       "            \"showSymbol\": true,\n",
       "            \"smooth\": false,\n",
       "            \"clip\": true,\n",
       "            \"step\": false,\n",
       "            \"data\": [\n",
       "                [\n",
       "                    \"2017-11-25\",\n",
       "                    0.4705\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-11-26\",\n",
       "                    0.4611\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-11-27\",\n",
       "                    0.4622\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-11-28\",\n",
       "                    0.4675\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-11-29\",\n",
       "                    0.4608\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-11-30\",\n",
       "                    0.4637\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-12-01\",\n",
       "                    0.4527\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-12-02\",\n",
       "                    0.4682\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-12-03\",\n",
       "                    0.8821\n",
       "                ]\n",
       "            ],\n",
       "            \"hoverAnimation\": true,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8,\n",
       "                \"formatter\": function(x){return Number(x.data[1] * 100).toFixed(2) + '%';}\n",
       "            },\n",
       "            \"lineStyle\": {\n",
       "                \"show\": true,\n",
       "                \"width\": 3,\n",
       "                \"opacity\": 1,\n",
       "                \"curveness\": 0,\n",
       "                \"type\": \"solid\"\n",
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       "            \"areaStyle\": {\n",
       "                \"opacity\": 0\n",
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       "            \"tooltip\": {\n",
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       "                \"trigger\": \"item\",\n",
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       "                \"alwaysShowContent\": false,\n",
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       "                \"formatter\": function(x){return Number(x.data[1] * 100).toFixed(2) + '%';},\n",
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       "                    \"fontSize\": 14\n",
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       "                \"borderWidth\": 0,\n",
       "                \"padding\": 5\n",
       "            },\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 0,\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u8df3\\u5931\\u7387\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"\\u8df3\\u5931\\u7387\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"xAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            },\n",
       "            \"data\": [\n",
       "                \"2017-11-25\",\n",
       "                \"2017-11-26\",\n",
       "                \"2017-11-27\",\n",
       "                \"2017-11-28\",\n",
       "                \"2017-11-29\",\n",
       "                \"2017-11-30\",\n",
       "                \"2017-12-01\",\n",
       "                \"2017-12-02\",\n",
       "                \"2017-12-03\"\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"axisLabel\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8,\n",
       "                \"formatter\": function(x){return Number(x * 100).toFixed() + '%'}\n",
       "            },\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u6bcf\\u65e5\\u8df3\\u5931\\u7387\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ],\n",
       "    \"toolbox\": {\n",
       "        \"show\": true,\n",
       "        \"orient\": \"horizontal\",\n",
       "        \"itemSize\": 15,\n",
       "        \"itemGap\": 10,\n",
       "        \"left\": \"80%\",\n",
       "        \"feature\": {\n",
       "            \"saveAsImage\": {\n",
       "                \"type\": \"png\",\n",
       "                \"backgroundColor\": \"auto\",\n",
       "                \"connectedBackgroundColor\": \"#fff\",\n",
       "                \"show\": true,\n",
       "                \"title\": \"\\u4fdd\\u5b58\\u4e3a\\u56fe\\u7247\",\n",
       "                \"pixelRatio\": 1\n",
       "            },\n",
       "            \"restore\": {\n",
       "                \"show\": true,\n",
       "                \"title\": \"\\u8fd8\\u539f\"\n",
       "            },\n",
       "            \"dataView\": {\n",
       "                \"show\": true,\n",
       "                \"title\": \"\\u6570\\u636e\\u89c6\\u56fe\",\n",
       "                \"readOnly\": false,\n",
       "                \"lang\": [\n",
       "                    \"\\u6570\\u636e\\u89c6\\u56fe\",\n",
       "                    \"\\u5173\\u95ed\",\n",
       "                    \"\\u5237\\u65b0\"\n",
       "                ],\n",
       "                \"backgroundColor\": \"#fff\",\n",
       "                \"textareaColor\": \"#fff\",\n",
       "                \"textareaBorderColor\": \"#333\",\n",
       "                \"textColor\": \"#000\",\n",
       "                \"buttonColor\": \"#c23531\",\n",
       "                \"buttonTextColor\": \"#fff\"\n",
       "            },\n",
       "            \"dataZoom\": {\n",
       "                \"show\": true,\n",
       "                \"title\": {\n",
       "                    \"zoom\": \"\\u533a\\u57df\\u7f29\\u653e\",\n",
       "                    \"back\": \"\\u533a\\u57df\\u7f29\\u653e\\u8fd8\\u539f\"\n",
       "                },\n",
       "                \"icon\": {},\n",
       "                \"xAxisIndex\": false,\n",
       "                \"yAxisIndex\": false,\n",
       "                \"filterMode\": \"filter\"\n",
       "            },\n",
       "            \"magicType\": {\n",
       "                \"show\": true,\n",
       "                \"type\": [\n",
       "                    \"line\",\n",
       "                    \"bar\",\n",
       "                    \"stack\",\n",
       "                    \"tiled\"\n",
       "                ],\n",
       "                \"title\": {\n",
       "                    \"line\": \"\\u5207\\u6362\\u4e3a\\u6298\\u7ebf\\u56fe\",\n",
       "                    \"bar\": \"\\u5207\\u6362\\u4e3a\\u67f1\\u72b6\\u56fe\",\n",
       "                    \"stack\": \"\\u5207\\u6362\\u4e3a\\u5806\\u53e0\",\n",
       "                    \"tiled\": \"\\u5207\\u6362\\u4e3a\\u5e73\\u94fa\"\n",
       "                },\n",
       "                \"icon\": {}\n",
       "            },\n",
       "            \"brush\": {\n",
       "                \"icon\": {},\n",
       "                \"title\": {\n",
       "                    \"rect\": \"\\u77e9\\u5f62\\u9009\\u62e9\",\n",
       "                    \"polygon\": \"\\u5708\\u9009\",\n",
       "                    \"lineX\": \"\\u6a2a\\u5411\\u9009\\u62e9\",\n",
       "                    \"lineY\": \"\\u7eb5\\u5411\\u9009\\u62e9\",\n",
       "                    \"keep\": \"\\u4fdd\\u6301\\u9009\\u62e9\",\n",
       "                    \"clear\": \"\\u6e05\\u9664\\u9009\\u62e9\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    },\n",
       "    \"dataZoom\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"type\": \"slider\",\n",
       "            \"realtime\": true,\n",
       "            \"start\": 0,\n",
       "            \"end\": 100,\n",
       "            \"orient\": \"horizontal\",\n",
       "            \"zoomLock\": false,\n",
       "            \"filterMode\": \"filter\"\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_57977501ada34d5dbe4a2dced69d2de9.setOption(option_57977501ada34d5dbe4a2dced69d2de9);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x23a18099d48>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    Line(init_opts=opts.InitOpts(theme=ThemeType.CHALK))\n",
    "    .add_xaxis(only_pv_df['日期'].dt.strftime('%Y-%m-%d').to_list())\n",
    "    .add_yaxis('跳失率', only_pv_df['跳失率'].to_list(),\n",
    "               symbol_size=10, # 设置点尺寸\n",
    "               linestyle_opts=opts.LineStyleOpts(width=3), # 设置线宽、颜色、线型\n",
    "               #label_opts=opts.LabelOpts(formatter=JsCode(\"function(x){return Number(x * 100).toFixed() + '%'}\"))\n",
    "              ) \n",
    "    .set_series_opts(\n",
    "        label_opts=opts.LabelOpts(\n",
    "            formatter=JsCode(\"function(x){return Number(x.data[1] * 100).toFixed(2) + '%';}\"), # Labels格式\n",
    "        ),\n",
    "        tooltip_opts=opts.TooltipOpts(\n",
    "            formatter=JsCode(\"function(x){return Number(x.data[1] * 100).toFixed(2) + '%';}\"), # Tooltip格式\n",
    "        )\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"每日跳失率\"),\n",
    "        yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter=JsCode(\"function(x){return Number(x * 100).toFixed() + '%'}\"))),\n",
    "        toolbox_opts=opts.ToolboxOpts(), # 工具箱\n",
    "        datazoom_opts=[opts.DataZoomOpts(is_realtime=True, range_start=0, range_end=100)],\n",
    "    )\n",
    "    .render_notebook()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图中可以看出，工作日的跳失率都在50%以下，周末的跳失率陡然升高，接近90%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 用户购买行为分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Access People on Daily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 46.4 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日期</th>\n",
       "      <th>pv</th>\n",
       "      <th>fac</th>\n",
       "      <th>cart</th>\n",
       "      <th>buy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>686953</td>\n",
       "      <td>115031</td>\n",
       "      <td>237553</td>\n",
       "      <td>132622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-11-26</td>\n",
       "      <td>695869</td>\n",
       "      <td>117699</td>\n",
       "      <td>247038</td>\n",
       "      <td>136048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-11-27</td>\n",
       "      <td>689260</td>\n",
       "      <td>114396</td>\n",
       "      <td>235841</td>\n",
       "      <td>146934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-11-28</td>\n",
       "      <td>688042</td>\n",
       "      <td>114014</td>\n",
       "      <td>234424</td>\n",
       "      <td>139845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>697542</td>\n",
       "      <td>116526</td>\n",
       "      <td>240008</td>\n",
       "      <td>146220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          日期      pv     fac    cart     buy\n",
       "0 2017-11-25  686953  115031  237553  132622\n",
       "1 2017-11-26  695869  117699  247038  136048\n",
       "2 2017-11-27  689260  114396  235841  146934\n",
       "3 2017-11-28  688042  114014  234424  139845\n",
       "4 2017-11-29  697542  116526  240008  146220"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "deal_daily = pd.read_sql_query(\"\"\"\n",
    "select\n",
    "    event_date as \"日期\",\n",
    "    count(distinct (case when behavior_type = 'pv' then user_id else null end)) as \"pv\",\n",
    "    count(distinct (case when behavior_type = 'fav' then user_id else null end)) as \"fac\",\n",
    "    count(distinct (case when behavior_type = 'cart' then user_id else null end)) as \"cart\",\n",
    "    count(distinct (case when behavior_type = 'buy' then user_id else null end)) as \"buy\"\n",
    "from user_behavior\n",
    "group by event_date\n",
    "order by event_date;\n",
    "\"\"\", con=engine, parse_dates=['日期'])\n",
    "deal_daily.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "<script>\n",
       "    require.config({\n",
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       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min', 'chalk':'https://assets.pyecharts.org/assets/themes/chalk'\n",
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       "</script>\n",
       "\n",
       "        <div id=\"b0965780508e4e738b5981f2779a7c5e\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
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       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"series\": [\n",
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       "                \"show\": true,\n",
       "                \"width\": 1,\n",
       "                \"opacity\": 1,\n",
       "                \"curveness\": 0,\n",
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       "            \"areaStyle\": {\n",
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       "            \"zlevel\": 0,\n",
       "            \"z\": 0\n",
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       "        {\n",
       "            \"type\": \"line\",\n",
       "            \"name\": \"cart\",\n",
       "            \"connectNulls\": false,\n",
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       "            \"showSymbol\": true,\n",
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       "        {\n",
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       "                    3\n",
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       "            ],\n",
       "            \"hoverAnimation\": true,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"lineStyle\": {\n",
       "                \"show\": true,\n",
       "                \"width\": 1,\n",
       "                \"opacity\": 1,\n",
       "                \"curveness\": 0,\n",
       "                \"type\": \"solid\"\n",
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       "            \"areaStyle\": {\n",
       "                \"opacity\": 0\n",
       "            },\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 0\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
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       "                \"pv\",\n",
       "                \"fac\",\n",
       "                \"cart\",\n",
       "                \"buy\"\n",
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       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
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       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"xAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
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       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
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       "                    \"opacity\": 1,\n",
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       "                    \"type\": \"solid\"\n",
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       "            },\n",
       "            \"data\": [\n",
       "                \"2017-11-25\",\n",
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       "            ]\n",
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       "    ],\n",
       "    \"yAxis\": [\n",
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       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"axisLabel\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8,\n",
       "                \"formatter\": function(x){return x.toExponential(2)}\n",
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       "            \"inverse\": false,\n",
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       "                    \"show\": true,\n",
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       "                }\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"Access People on Daily\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
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       "        \"show\": true,\n",
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       "                \"show\": true,\n",
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       "                ],\n",
       "                \"title\": {\n",
       "                    \"line\": \"\\u5207\\u6362\\u4e3a\\u6298\\u7ebf\\u56fe\",\n",
       "                    \"bar\": \"\\u5207\\u6362\\u4e3a\\u67f1\\u72b6\\u56fe\",\n",
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       "                    \"tiled\": \"\\u5207\\u6362\\u4e3a\\u5e73\\u94fa\"\n",
       "                },\n",
       "                \"icon\": {}\n",
       "            },\n",
       "            \"brush\": {\n",
       "                \"icon\": {},\n",
       "                \"title\": {\n",
       "                    \"rect\": \"\\u77e9\\u5f62\\u9009\\u62e9\",\n",
       "                    \"polygon\": \"\\u5708\\u9009\",\n",
       "                    \"lineX\": \"\\u6a2a\\u5411\\u9009\\u62e9\",\n",
       "                    \"lineY\": \"\\u7eb5\\u5411\\u9009\\u62e9\",\n",
       "                    \"keep\": \"\\u4fdd\\u6301\\u9009\\u62e9\",\n",
       "                    \"clear\": \"\\u6e05\\u9664\\u9009\\u62e9\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    },\n",
       "    \"dataZoom\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"type\": \"slider\",\n",
       "            \"realtime\": true,\n",
       "            \"start\": 0,\n",
       "            \"end\": 100,\n",
       "            \"orient\": \"horizontal\",\n",
       "            \"zoomLock\": false,\n",
       "            \"filterMode\": \"filter\"\n",
       "        },\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"type\": \"slider\",\n",
       "            \"realtime\": true,\n",
       "            \"start\": 0,\n",
       "            \"end\": 100,\n",
       "            \"orient\": \"vertical\",\n",
       "            \"zoomLock\": false,\n",
       "            \"filterMode\": \"filter\"\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_b0965780508e4e738b5981f2779a7c5e.setOption(option_b0965780508e4e738b5981f2779a7c5e);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x23a18095b48>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    Line(init_opts=opts.InitOpts(theme=ThemeType.CHALK))\n",
    "    .add_xaxis(deal_daily['日期'].dt.strftime('%Y-%m-%d').to_list())\n",
    "    .add_yaxis(\"pv\", deal_daily['pv'].to_list()) \n",
    "    .add_yaxis(\"fac\", deal_daily['fac'].to_list()) \n",
    "    .add_yaxis(\"cart\", deal_daily['cart'].to_list()) \n",
    "    .add_yaxis(\"buy\", deal_daily['buy'].to_list()) \n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"Access People on Daily\"),\n",
    "        yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter=JsCode(\"function(x){return x.toExponential(2)}\"))),\n",
    "        toolbox_opts=opts.ToolboxOpts(), # 工具箱\n",
    "        datazoom_opts=[opts.DataZoomOpts(is_realtime=True, range_start=0, range_end=100), \n",
    "                       opts.DataZoomOpts(orient=\"vertical\", range_start=0, range_end=100)], # 同时添加水平和垂直滑动条\n",
    "    )\n",
    "    .render_notebook()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Access People on Hourly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 44.9 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>时</th>\n",
       "      <th>pv</th>\n",
       "      <th>fac</th>\n",
       "      <th>cart</th>\n",
       "      <th>buy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>271467</td>\n",
       "      <td>39386</td>\n",
       "      <td>76187</td>\n",
       "      <td>33847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>140790</td>\n",
       "      <td>19496</td>\n",
       "      <td>36867</td>\n",
       "      <td>13612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>82507</td>\n",
       "      <td>10685</td>\n",
       "      <td>20776</td>\n",
       "      <td>7156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>60125</td>\n",
       "      <td>7342</td>\n",
       "      <td>14843</td>\n",
       "      <td>4823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>54325</td>\n",
       "      <td>6219</td>\n",
       "      <td>13247</td>\n",
       "      <td>4080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   时      pv    fac   cart    buy\n",
       "0  0  271467  39386  76187  33847\n",
       "1  1  140790  19496  36867  13612\n",
       "2  2   82507  10685  20776   7156\n",
       "3  3   60125   7342  14843   4823\n",
       "4  4   54325   6219  13247   4080"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "deal_hourly = pd.read_sql_query(\"\"\"\n",
    "select\n",
    "    event_hour as \"时\",\n",
    "    count(distinct (case when behavior_type = 'pv' then user_id else null end)) as \"pv\",\n",
    "    count(distinct (case when behavior_type = 'fav' then user_id else null end)) as \"fac\",\n",
    "    count(distinct (case when behavior_type = 'cart' then user_id else null end)) as \"cart\",\n",
    "    count(distinct (case when behavior_type = 'buy' then user_id else null end)) as \"buy\"\n",
    "from user_behavior\n",
    "group by event_hour\n",
    "order by event_hour;\n",
    "\"\"\", con=engine, parse_dates=['日期'])\n",
    "deal_hourly.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "                \"show\": true,\n",
       "                \"title\": \"\\u8fd8\\u539f\"\n",
       "            },\n",
       "            \"dataView\": {\n",
       "                \"show\": true,\n",
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       "                \"readOnly\": false,\n",
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       "                    \"\\u6570\\u636e\\u89c6\\u56fe\",\n",
       "                    \"\\u5173\\u95ed\",\n",
       "                    \"\\u5237\\u65b0\"\n",
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       "                \"backgroundColor\": \"#fff\",\n",
       "                \"textareaColor\": \"#fff\",\n",
       "                \"textareaBorderColor\": \"#333\",\n",
       "                \"textColor\": \"#000\",\n",
       "                \"buttonColor\": \"#c23531\",\n",
       "                \"buttonTextColor\": \"#fff\"\n",
       "            },\n",
       "            \"dataZoom\": {\n",
       "                \"show\": true,\n",
       "                \"title\": {\n",
       "                    \"zoom\": \"\\u533a\\u57df\\u7f29\\u653e\",\n",
       "                    \"back\": \"\\u533a\\u57df\\u7f29\\u653e\\u8fd8\\u539f\"\n",
       "                },\n",
       "                \"icon\": {},\n",
       "                \"xAxisIndex\": false,\n",
       "                \"yAxisIndex\": false,\n",
       "                \"filterMode\": \"filter\"\n",
       "            },\n",
       "            \"magicType\": {\n",
       "                \"show\": true,\n",
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       "                    \"line\",\n",
       "                    \"bar\",\n",
       "                    \"stack\",\n",
       "                    \"tiled\"\n",
       "                ],\n",
       "                \"title\": {\n",
       "                    \"line\": \"\\u5207\\u6362\\u4e3a\\u6298\\u7ebf\\u56fe\",\n",
       "                    \"bar\": \"\\u5207\\u6362\\u4e3a\\u67f1\\u72b6\\u56fe\",\n",
       "                    \"stack\": \"\\u5207\\u6362\\u4e3a\\u5806\\u53e0\",\n",
       "                    \"tiled\": \"\\u5207\\u6362\\u4e3a\\u5e73\\u94fa\"\n",
       "                },\n",
       "                \"icon\": {}\n",
       "            },\n",
       "            \"brush\": {\n",
       "                \"icon\": {},\n",
       "                \"title\": {\n",
       "                    \"rect\": \"\\u77e9\\u5f62\\u9009\\u62e9\",\n",
       "                    \"polygon\": \"\\u5708\\u9009\",\n",
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       "                    \"lineY\": \"\\u7eb5\\u5411\\u9009\\u62e9\",\n",
       "                    \"keep\": \"\\u4fdd\\u6301\\u9009\\u62e9\",\n",
       "                    \"clear\": \"\\u6e05\\u9664\\u9009\\u62e9\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    },\n",
       "    \"dataZoom\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"type\": \"slider\",\n",
       "            \"realtime\": true,\n",
       "            \"start\": 0,\n",
       "            \"end\": 100,\n",
       "            \"orient\": \"horizontal\",\n",
       "            \"zoomLock\": false,\n",
       "            \"filterMode\": \"filter\"\n",
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       "        {\n",
       "            \"show\": true,\n",
       "            \"type\": \"slider\",\n",
       "            \"realtime\": true,\n",
       "            \"start\": 0,\n",
       "            \"end\": 100,\n",
       "            \"orient\": \"vertical\",\n",
       "            \"zoomLock\": false,\n",
       "            \"filterMode\": \"filter\"\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_9b3ae6dc62cc4b86bd584c5c52c9a27e.setOption(option_9b3ae6dc62cc4b86bd584c5c52c9a27e);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x23a18098d48>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    Line(init_opts=opts.InitOpts(theme=ThemeType.CHALK))\n",
    "    .add_xaxis(deal_hourly['时'].to_list())\n",
    "    .add_yaxis(\"pv\", deal_hourly['pv'].to_list()) \n",
    "    .add_yaxis(\"fac\", deal_hourly['fac'].to_list()) \n",
    "    .add_yaxis(\"cart\", deal_hourly['cart'].to_list()) \n",
    "    .add_yaxis(\"buy\", deal_hourly['buy'].to_list()) \n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"Access People on Hourly\"),\n",
    "        toolbox_opts=opts.ToolboxOpts(), # 工具箱\n",
    "        yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter=JsCode(\"function(x){return x.toExponential(2)}\"))),\n",
    "        datazoom_opts=[opts.DataZoomOpts(is_realtime=True, range_start=0, range_end=100), \n",
    "                       opts.DataZoomOpts(orient=\"vertical\", range_start=0, range_end=100)], # 同时添加水平和垂直滑动条\n",
    "    )\n",
    "    .render_notebook()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 店铺复购率\n",
    "复购率 = 购买次数两次及以上的人数/有购买行为的用户总数  \n",
    "这里还有1个前提条件，即定义购买次数，就需要确定1个时间范围，计算该时间范围内的复购率。  \n",
    "复购率分为**店铺复购率**和**商品复购率**， 这里假设所有商品都来自1家店铺, 否则需要对店铺进行筛选或分组。后面有章节介绍**商品复购率**。\n",
    "如果**店铺复购率**高，可以使用RFM模型对用户价值进行划分等级，详情见RFM分析相关教程。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**店铺复购率计算如下:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "engine.execute(\"\"\"\n",
    "create or replace function get_repurchase_rate_of_shop\n",
    "(\n",
    "    start_date1 in date default null,\n",
    "    end_date1 in date default null,\n",
    "    repurchase_rate out numeric\n",
    ")\n",
    "as $$\n",
    "    declare start_date2 date;\n",
    "    declare end_date2 date;\n",
    "begin\n",
    "    if start_date1 is null then\n",
    "        select min(event_date) into start_date2 from user_behavior;\n",
    "    else\n",
    "        start_date2 := start_date1;\n",
    "    end if;\n",
    "    if end_date1 is null then\n",
    "        end_date2 := current_date;\n",
    "    else\n",
    "        end_date2 := end_date1;\n",
    "    end if;\n",
    "    if start_date2 > end_date2 then\n",
    "        raise exception '结束日期\"%\"必须大于开始日期\"%\"!', end_date2, start_date2;\n",
    "    end if;\n",
    "    select\n",
    "        round(\n",
    "            cast(sum(case when buy_num >= 2 then 1 else 0 end) as numeric)/\n",
    "            cast(count(user_id) as numeric)\n",
    "            , 4\n",
    "            ) into repurchase_rate\n",
    "    from\n",
    "    (\n",
    "        select\n",
    "            user_id,\n",
    "            count(*) as buy_num\n",
    "        from user_behavior\n",
    "        where behavior_type = 'buy' and (event_date between start_date2 and end_date2)\n",
    "        group by user_id\n",
    "    ) ug;\n",
    "end;\n",
    "$$ language plpgsql;\n",
    "\"\"\")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Decimal('0.6278')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Decimal('0.5936')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 所有时间范围复购率\n",
    "engine.execute(\"\"\"\n",
    "select * from get_repurchase_rate_of_shop(null, null);\n",
    "\"\"\").fetchone()[0]\n",
    "# 截至时间2017年12月1日复购率\n",
    "engine.execute(\"\"\"\n",
    "select * from get_repurchase_rate_of_shop(null, '2017-12-01');\n",
    "\"\"\").fetchone()[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "复购率较高，RFM模型对用户价值进行划分不作赘述。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 漏斗模型\n",
    "建立用户各行为转化漏斗模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用户行为计数\n",
    "用户行为计数同样需要确实日期范围，不妨创建1个函数，输入日期范围，默认开始日期为最小值，默认结束日期为今天。\n",
    "这样方便对比2个时间段的数据。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "engine.execute(\"\"\"\n",
    "create or replace function get_behavior_of_user\n",
    "(\n",
    "    start_date1 date default null,\n",
    "    end_date1 date default null\n",
    ")\n",
    "returns table\n",
    "(\n",
    "    \"用户编号\" int,\n",
    "    \"用户行为总数\" bigint,\n",
    "    \"pv\" bigint,\n",
    "    \"fac\" bigint,\n",
    "    \"cart\" bigint,\n",
    "    \"buy\" bigint\n",
    ")\n",
    "as $$\n",
    "    declare start_date2 date;\n",
    "    declare end_date2 date;\n",
    "begin\n",
    "    if start_date1 is null then\n",
    "        select min(event_date) into start_date2 from user_behavior;\n",
    "    else\n",
    "        start_date2 := start_date1;\n",
    "    end if;\n",
    "    if end_date1 is null then\n",
    "        end_date2 := current_date;\n",
    "    else\n",
    "        end_date2 := end_date1;\n",
    "    end if;\n",
    "    if start_date2 > end_date2 then\n",
    "        raise exception '结束日期\"%\"必须大于开始日期\"%\"!', end_date2, start_date2;\n",
    "    end if;\n",
    "    return query select\n",
    "        user_id,\n",
    "        count(*) as all_bhv,\n",
    "        sum(case when behavior_type = 'pv' then 1 else 0 end),\n",
    "        sum(case when behavior_type = 'fac' then 1 else 0 end),\n",
    "        sum(case when behavior_type = 'cart' then 1 else 0 end),\n",
    "        sum(case when behavior_type = 'buy' then 1 else 0 end)\n",
    "    from user_behavior\n",
    "    where event_date between start_date2 and end_date2\n",
    "    group by user_id\n",
    "    order by all_bhv desc;\n",
    "end;\n",
    "$$ language plpgsql;\n",
    "\"\"\")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 15s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户编号</th>\n",
       "      <th>用户行为总数</th>\n",
       "      <th>pv</th>\n",
       "      <th>fac</th>\n",
       "      <th>cart</th>\n",
       "      <th>buy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54206</td>\n",
       "      <td>792</td>\n",
       "      <td>776</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>208813</td>\n",
       "      <td>789</td>\n",
       "      <td>742</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>503757</td>\n",
       "      <td>780</td>\n",
       "      <td>756</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>996214</td>\n",
       "      <td>773</td>\n",
       "      <td>758</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>886611</td>\n",
       "      <td>773</td>\n",
       "      <td>772</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     用户编号  用户行为总数   pv  fac  cart  buy\n",
       "0   54206     792  776    0    15    0\n",
       "1  208813     789  742    0    45    2\n",
       "2  503757     780  756    0     8    6\n",
       "3  996214     773  758    0    14    1\n",
       "4  886611     773  772    0     0    1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "behavior_of_user_df = pd.read_sql_query(\"\"\"\n",
    "select * from get_behavior_of_user(null, null)\n",
    "\"\"\", con=engine)\n",
    "behavior_of_user_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用户各行为发生总数\n",
    "临时的，下面计算转化率会有正式的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 12s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_user</th>\n",
       "      <th>total_behave</th>\n",
       "      <th>total_pv</th>\n",
       "      <th>total_fac</th>\n",
       "      <th>total_cart</th>\n",
       "      <th>total_buy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>987990</td>\n",
       "      <td>86433301.0</td>\n",
       "      <td>77423590.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4755556.0</td>\n",
       "      <td>1758085.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_user  total_behave    total_pv  total_fac  total_cart  total_buy\n",
       "0      987990    86433301.0  77423590.0        0.0   4755556.0  1758085.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "behavior_all_df = pd.read_sql_query(\"\"\"\n",
    "select\n",
    "    count(\"用户编号\") as total_user,\n",
    "    sum(\"用户行为总数\") as total_behave,\n",
    "    sum(pv) as total_pv,\n",
    "    sum(fac) as total_fac,\n",
    "    sum(cart) as total_cart,\n",
    "    sum(buy) as total_buy\n",
    "from get_behavior_of_user(null, null)\n",
    "\"\"\", con=engine)\n",
    "behavior_all_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用户各行为的转化率\n",
    "转化率同样需要确实日期范围，不妨创建1个函数，输入日期范围，默认开始日期为最小值，默认结束日期为今天。\n",
    "这样方便对比2个时间段的数据。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```sql\n",
    "create or replace function get_behave_conversion_rate\n",
    "(\n",
    "    start_date1 date default null,\n",
    "    end_date1 date default null\n",
    ")\n",
    "returns table\n",
    "(\n",
    "    \"用户数量\" bigint,\n",
    "    \"用户行为总数\" bigint,\n",
    "    \"total_pv\" bigint,\n",
    "    \"total_fac\" bigint,\n",
    "    \"total_cart\" bigint,\n",
    "    \"total_buy\" bigint,\n",
    "    \"behave_to_pv\" numeric,\n",
    "    \"pv_to_facCart\" numeric,\n",
    "    \"pv_to_buy\" numeric\n",
    ")\n",
    "as $$\n",
    "    declare start_date2 date;\n",
    "    declare end_date2 date;\n",
    "begin\n",
    "    if start_date1 is null then\n",
    "        select min(event_date) into start_date2 from user_behavior;\n",
    "    else\n",
    "        start_date2 := start_date1;\n",
    "    end if;\n",
    "    if end_date1 is null then\n",
    "        end_date2 := current_date;\n",
    "    else\n",
    "        end_date2 := end_date1;\n",
    "    end if;\n",
    "    if start_date2 > end_date2 then\n",
    "        raise exception '结束日期\"%\"必须大于开始日期\"%\"!', end_date2, start_date2;\n",
    "    end if;\n",
    "    return query select\n",
    "        t.total_user,\n",
    "        t.total_behave,\n",
    "        t.total_pv,\n",
    "        t.total_fac,\n",
    "        t.total_cart,\n",
    "        t.total_buy,\n",
    "        round(cast(t.total_pv as numeric) / cast(t.total_behave as numeric), 4),\n",
    "        round(cast((t.total_fac + t.total_cart) as numeric) / cast(t.total_pv as numeric), 4),\n",
    "        round(cast(t.total_buy as numeric) / cast(t.total_pv as numeric), 4)\n",
    "    from\n",
    "    (\n",
    "        select\n",
    "            count(distinct user_id) as total_user,\n",
    "            count(behavior_type) as total_behave,\n",
    "            sum(case when behavior_type = 'pv' then 1 else 0 end) as total_pv,\n",
    "            sum(case when behavior_type = 'fac' then 1 else 0 end) as total_fac,\n",
    "            sum(case when behavior_type = 'cart' then 1 else 0 end) as total_cart,\n",
    "            sum(case when behavior_type = 'buy' then 1 else 0 end) as total_buy\n",
    "        from user_behavior\n",
    "        where event_date between start_date2 and end_date2\n",
    "    ) t;\n",
    "end;\n",
    "$$ language plpgsql;\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 45.5 s\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户数量</th>\n",
       "      <th>用户行为总数</th>\n",
       "      <th>total_pv</th>\n",
       "      <th>total_fac</th>\n",
       "      <th>total_cart</th>\n",
       "      <th>total_buy</th>\n",
       "      <th>behave_to_pv</th>\n",
       "      <th>pv_to_facCart</th>\n",
       "      <th>pv_to_buy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>987990</td>\n",
       "      <td>86433301</td>\n",
       "      <td>77423590</td>\n",
       "      <td>0</td>\n",
       "      <td>4755556</td>\n",
       "      <td>1758085</td>\n",
       "      <td>0.8958</td>\n",
       "      <td>0.0614</td>\n",
       "      <td>0.0227</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     用户数量    用户行为总数  total_pv  total_fac  total_cart  total_buy  behave_to_pv  \\\n",
       "0  987990  86433301  77423590          0     4755556    1758085        0.8958   \n",
       "\n",
       "   pv_to_facCart  pv_to_buy  \n",
       "0         0.0614     0.0227  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "conversion_rate_df = pd.read_sql_query(\"\"\"\n",
    "select * from get_behave_conversion_rate(null, null)\n",
    "\"\"\", con=engine)\n",
    "conversion_rate_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"70a1157513134814907c5cb1f2f05ca9\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_70a1157513134814907c5cb1f2f05ca9 = echarts.init(\n",
       "                    document.getElementById('70a1157513134814907c5cb1f2f05ca9'), 'white', {renderer: 'canvas'});\n",
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       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
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       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
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       "        \"#ef5b9c\",\n",
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       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
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       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"funnel\",\n",
       "            \"name\": \"\\u8f6c\\u5316\\u7387\",\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"name\": \"\\u70b9\\u51fb\",\n",
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       "                },\n",
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       "                {\n",
       "                    \"name\": \"\\u8d2d\\u4e70\",\n",
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       "            \"sort\": \"descending\",\n",
       "            \"gap\": 0,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
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       "        }\n",
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       "    \"legend\": [\n",
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       "            \"show\": true,\n",
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       "            \"itemGap\": 10,\n",
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       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
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       "        \"hideDelay\": 100,\n",
       "        \"formatter\": function(x){return x.name + ': ' + Number(x.value * 100).toFixed(2) + '%';},\n",
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       "            \"fontSize\": 14\n",
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       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
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       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u7528\\u6237\\u884c\\u4e3a\\u8f6c\\u5316\\u7387\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_70a1157513134814907c5cb1f2f05ca9.setOption(option_70a1157513134814907c5cb1f2f05ca9);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x23a17fd2048>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_for_funnel = conversion_rate_df[['behave_to_pv', 'pv_to_facCart', 'pv_to_buy']].values[0].tolist()\n",
    "data_for_funnel = [list(x) for x in zip(['点击', '收藏&加入购物车', '购买'], data_for_funnel)]\n",
    "(\n",
    "    Funnel()\n",
    "    .add(\"转化率\", data_for_funnel)\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"用户行为转化率\"),\n",
    "        tooltip_opts=opts.TooltipOpts(\n",
    "            formatter=JsCode(\n",
    "                \"function(x){return x.name + ': ' + Number(x.value * 100).toFixed(2) + '%';}\"), # tooltip格式\n",
    "        )\n",
    "    )\n",
    "    .render_notebook()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 用户留存分析\n",
    "通常将'次日留存率'、‘三日留存率’和'七日留存率'作为留存指标。  \n",
    "网上很多教程这里计算都是错的，应该用留存数量除以3天前的新用户数量，这样才能得到当天的‘三日留存率’。'次日留存率'和'七日留存率'类似。\n",
    "虽然可以使用多次子查询计算留存率，但是计算效率低，建立1个中间表和视图，这样更快。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 38s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日期</th>\n",
       "      <th>新用户</th>\n",
       "      <th>次日留存</th>\n",
       "      <th>三日留存</th>\n",
       "      <th>七日留存</th>\n",
       "      <th>次日留存率</th>\n",
       "      <th>三日留存率</th>\n",
       "      <th>七日留存率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>706641</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-11-26</td>\n",
       "      <td>158188</td>\n",
       "      <td>557328</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7887</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-11-27</td>\n",
       "      <td>63825</td>\n",
       "      <td>103431</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-11-28</td>\n",
       "      <td>31331</td>\n",
       "      <td>39280</td>\n",
       "      <td>536463</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6154</td>\n",
       "      <td>0.7592</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>17931</td>\n",
       "      <td>19366</td>\n",
       "      <td>104190</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6181</td>\n",
       "      <td>0.6586</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          日期     新用户    次日留存    三日留存  七日留存   次日留存率   三日留存率  七日留存率\n",
       "0 2017-11-25  706641       0       0     0     NaN     NaN    NaN\n",
       "1 2017-11-26  158188  557328       0     0  0.7887     NaN    NaN\n",
       "2 2017-11-27   63825  103431       0     0  0.6538     NaN    NaN\n",
       "3 2017-11-28   31331   39280  536463     0  0.6154  0.7592    NaN\n",
       "4 2017-11-29   17931   19366  104190     0  0.6181  0.6586    NaN"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "liucun_df = pd.read_sql_query(\"\"\"\n",
    "with liucun as \n",
    "(\n",
    "    select\n",
    "        event_date,\n",
    "        (case when sum(new_user) = 0 then null else sum(new_user) end) as new_user,\n",
    "        sum(liucun1) as liucun1,\n",
    "        sum(liucun3) as liucun3,\n",
    "        sum(liucun7) as liucun7\n",
    "    from\n",
    "    (\n",
    "        select\n",
    "            -- 多变量去重\n",
    "            distinct user_id, event_date, new_user, liucun1, liucun3, liucun7\n",
    "        from\n",
    "        (\n",
    "            select\n",
    "                user_id,\n",
    "                event_date,\n",
    "                (case when event_date = first_date then 1 else 0 end) as new_user,\n",
    "                (case when event_date = first_date + interval '1 day' then 1 else 0 end) as liucun1,\n",
    "                (case when event_date = first_date + interval '3 days' then 1 else 0 end) as liucun3,\n",
    "                (case when event_date = first_date + interval '7 days' then 1 else 0 end) as liucun7\n",
    "            from\n",
    "            (\n",
    "                select\n",
    "                    user_id,\n",
    "                    event_date,\n",
    "                    min(event_date) over (partition by user_id) as first_date\n",
    "                from user_behavior\n",
    "            ) t1\n",
    "        ) t2\n",
    "    ) t3\n",
    "    group by event_date\n",
    "    order by event_date\n",
    ")\n",
    "-- 计算留存率\n",
    "select\n",
    "    t0.event_date as \"日期\",\n",
    "    coalesce(t0.new_user, 0) as \"新用户\",\n",
    "    t0.liucun1 as \"次日留存\",\n",
    "    t0.liucun3 as \"三日留存\",\n",
    "    t0.liucun7 as \"七日留存\",\n",
    "    round(cast(t0.liucun1 as numeric) / cast(t1.new_user as numeric), 4) as \"次日留存率\",\n",
    "    round(cast(t0.liucun3 as numeric) / cast(t3.new_user as numeric), 4) as \"三日留存率\",\n",
    "    round(cast(t0.liucun7 as numeric) / cast(t7.new_user as numeric), 4) as \"七日留存率\"\n",
    "from\n",
    "(\n",
    "    select\n",
    "        event_date,\n",
    "        new_user,\n",
    "        liucun1,\n",
    "        liucun3,\n",
    "        liucun7,\n",
    "        event_date - interval '1 day' as date1,\n",
    "        event_date - interval '3 day' as date3,\n",
    "        event_date - interval '7 day' as date7\n",
    "    from liucun\n",
    ") t0\n",
    "left join liucun t1\n",
    "    on t0.date1 = t1.event_date\n",
    "left join liucun t3\n",
    "    on t0.date3 = t3.event_date\n",
    "left join liucun t7\n",
    "    on t0.date7 = t7.event_date;\n",
    "\"\"\", con=engine, parse_dates=['日期'])\n",
    "\n",
    "liucun_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "<script>\n",
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       "                    document.getElementById('c0db2ae0245d48148cc72f3a0e50d398'), 'chalk', {renderer: 'canvas'});\n",
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       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"series\": [\n",
       "        {\n",
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       "                \"curveness\": 0,\n",
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       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
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       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
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       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"xAxis\": [\n",
       "        {\n",
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       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
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       "            \"splitLine\": {\n",
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       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
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       "            \"data\": [\n",
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       "                \"2017-11-26\",\n",
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       "                \"2017-12-01\",\n",
       "                \"2017-12-02\",\n",
       "                \"2017-12-03\"\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"axisLabel\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8,\n",
       "                \"formatter\": function(x){return x.toExponential(2)}\n",
       "            },\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
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       "            }\n",
       "        }\n",
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       "        {\n",
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       "            \"padding\": 5,\n",
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       "    \"toolbox\": {\n",
       "        \"show\": true,\n",
       "        \"orient\": \"horizontal\",\n",
       "        \"itemSize\": 15,\n",
       "        \"itemGap\": 10,\n",
       "        \"left\": \"80%\",\n",
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       "            \"restore\": {\n",
       "                \"show\": true,\n",
       "                \"title\": \"\\u8fd8\\u539f\"\n",
       "            },\n",
       "            \"dataView\": {\n",
       "                \"show\": true,\n",
       "                \"title\": \"\\u6570\\u636e\\u89c6\\u56fe\",\n",
       "                \"readOnly\": false,\n",
       "                \"lang\": [\n",
       "                    \"\\u6570\\u636e\\u89c6\\u56fe\",\n",
       "                    \"\\u5173\\u95ed\",\n",
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       "                \"backgroundColor\": \"#fff\",\n",
       "                \"textareaColor\": \"#fff\",\n",
       "                \"textareaBorderColor\": \"#333\",\n",
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       "                \"buttonColor\": \"#c23531\",\n",
       "                \"buttonTextColor\": \"#fff\"\n",
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       "            \"dataZoom\": {\n",
       "                \"show\": true,\n",
       "                \"title\": {\n",
       "                    \"zoom\": \"\\u533a\\u57df\\u7f29\\u653e\",\n",
       "                    \"back\": \"\\u533a\\u57df\\u7f29\\u653e\\u8fd8\\u539f\"\n",
       "                },\n",
       "                \"icon\": {},\n",
       "                \"xAxisIndex\": false,\n",
       "                \"yAxisIndex\": false,\n",
       "                \"filterMode\": \"filter\"\n",
       "            },\n",
       "            \"magicType\": {\n",
       "                \"show\": true,\n",
       "                \"type\": [\n",
       "                    \"line\",\n",
       "                    \"bar\",\n",
       "                    \"stack\",\n",
       "                    \"tiled\"\n",
       "                ],\n",
       "                \"title\": {\n",
       "                    \"line\": \"\\u5207\\u6362\\u4e3a\\u6298\\u7ebf\\u56fe\",\n",
       "                    \"bar\": \"\\u5207\\u6362\\u4e3a\\u67f1\\u72b6\\u56fe\",\n",
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       "                    \"tiled\": \"\\u5207\\u6362\\u4e3a\\u5e73\\u94fa\"\n",
       "                },\n",
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       "            \"brush\": {\n",
       "                \"icon\": {},\n",
       "                \"title\": {\n",
       "                    \"rect\": \"\\u77e9\\u5f62\\u9009\\u62e9\",\n",
       "                    \"polygon\": \"\\u5708\\u9009\",\n",
       "                    \"lineX\": \"\\u6a2a\\u5411\\u9009\\u62e9\",\n",
       "                    \"lineY\": \"\\u7eb5\\u5411\\u9009\\u62e9\",\n",
       "                    \"keep\": \"\\u4fdd\\u6301\\u9009\\u62e9\",\n",
       "                    \"clear\": \"\\u6e05\\u9664\\u9009\\u62e9\"\n",
       "                }\n",
       "            }\n",
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       "    },\n",
       "    \"dataZoom\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"type\": \"slider\",\n",
       "            \"realtime\": true,\n",
       "            \"start\": 0,\n",
       "            \"end\": 100,\n",
       "            \"orient\": \"horizontal\",\n",
       "            \"zoomLock\": false,\n",
       "            \"filterMode\": \"filter\"\n",
       "        },\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"type\": \"slider\",\n",
       "            \"realtime\": true,\n",
       "            \"start\": 0,\n",
       "            \"end\": 100,\n",
       "            \"orient\": \"vertical\",\n",
       "            \"zoomLock\": false,\n",
       "            \"filterMode\": \"filter\"\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_c0db2ae0245d48148cc72f3a0e50d398.setOption(option_c0db2ae0245d48148cc72f3a0e50d398);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x23a20964f88>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    Line(init_opts=opts.InitOpts(theme=ThemeType.CHALK))\n",
    "    .add_xaxis(liucun_df['日期'].dt.strftime('%Y-%m-%d').to_list())\n",
    "    .add_yaxis('新用户', liucun_df['新用户'].to_list()) \n",
    "    .add_yaxis('次日留存', liucun_df['次日留存'].to_list()) \n",
    "    .add_yaxis('三日留存', liucun_df['三日留存'].to_list()) \n",
    "    .add_yaxis('七日留存', liucun_df['七日留存'].to_list())\n",
    "    .set_series_opts(\n",
    "        label_opts=opts.LabelOpts(\n",
    "            formatter=JsCode(\"function(x){return x.data[1].toExponential(2);}\"), # Labels格式\n",
    "        )\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"每日用户留存\"),\n",
    "        yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter=JsCode(\"function(x){return x.toExponential(2)}\"))),\n",
    "        toolbox_opts=opts.ToolboxOpts(), # 工具箱\n",
    "        datazoom_opts=[opts.DataZoomOpts(is_realtime=True, range_start=0, range_end=100), \n",
    "                       opts.DataZoomOpts(orient=\"vertical\", range_start=0, range_end=100)], # 同时添加水平和垂直滑动条\n",
    "    )\n",
    "    .render_notebook()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min', 'chalk':'https://assets.pyecharts.org/assets/themes/chalk'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"3260ee98df4c40de9b679c4cf0d676a5\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts', 'chalk'], function(echarts) {\n",
       "                var chart_3260ee98df4c40de9b679c4cf0d676a5 = echarts.init(\n",
       "                    document.getElementById('3260ee98df4c40de9b679c4cf0d676a5'), 'chalk', {renderer: 'canvas'});\n",
       "                var option_3260ee98df4c40de9b679c4cf0d676a5 = {\n",
       "    \"animation\": true,\n",
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       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
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       "                    \"2017-12-02\",\n",
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       "                ],\n",
       "                [\n",
       "                    \"2017-12-03\",\n",
       "                    0.0\n",
       "                ]\n",
       "            ],\n",
       "            \"hoverAnimation\": true,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8,\n",
       "                \"formatter\": function(x){return Number(x.data[1] * 100).toFixed(2) + '%';}\n",
       "            },\n",
       "            \"lineStyle\": {\n",
       "                \"show\": true,\n",
       "                \"width\": 1,\n",
       "                \"opacity\": 1,\n",
       "                \"curveness\": 0,\n",
       "                \"type\": \"solid\"\n",
       "            },\n",
       "            \"areaStyle\": {\n",
       "                \"opacity\": 0\n",
       "            },\n",
       "            \"tooltip\": {\n",
       "                \"show\": true,\n",
       "                \"trigger\": \"item\",\n",
       "                \"triggerOn\": \"mousemove|click\",\n",
       "                \"axisPointer\": {\n",
       "                    \"type\": \"line\"\n",
       "                },\n",
       "                \"showContent\": true,\n",
       "                \"alwaysShowContent\": false,\n",
       "                \"showDelay\": 0,\n",
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       "                \"formatter\": function(x){return Number(x.data[1] * 100).toFixed(2) + '%';},\n",
       "                \"textStyle\": {\n",
       "                    \"fontSize\": 14\n",
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       "                \"borderWidth\": 0,\n",
       "                \"padding\": 5\n",
       "            },\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 0,\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
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       "        {\n",
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       "            \"connectNulls\": false,\n",
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       "            \"hoverAnimation\": true,\n",
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       "                \"formatter\": function(x){return Number(x.data[1] * 100).toFixed(2) + '%';}\n",
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       "                \"curveness\": 0,\n",
       "                \"type\": \"solid\"\n",
       "            },\n",
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       "            },\n",
       "            \"tooltip\": {\n",
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       "                \"formatter\": function(x){return Number(x.data[1] * 100).toFixed(2) + '%';},\n",
       "                \"textStyle\": {\n",
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       "            },\n",
       "            \"zlevel\": 0,\n",
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       "                \"\\u4e09\\u65e5\\u7559\\u5b58\\u7387\": true,\n",
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       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"xAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
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       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            },\n",
       "            \"data\": [\n",
       "                \"2017-11-25\",\n",
       "                \"2017-11-26\",\n",
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       "                \"2017-11-28\",\n",
       "                \"2017-11-29\",\n",
       "                \"2017-11-30\",\n",
       "                \"2017-12-01\",\n",
       "                \"2017-12-02\",\n",
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       "        }\n",
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       "    \"yAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"axisLabel\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8,\n",
       "                \"formatter\": function(x){return Number(x * 100).toFixed() + '%'}\n",
       "            },\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
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       "                    \"type\": \"solid\"\n",
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       "                    \"lineY\": \"\\u7eb5\\u5411\\u9009\\u62e9\",\n",
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       "                    \"clear\": \"\\u6e05\\u9664\\u9009\\u62e9\"\n",
       "                }\n",
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       "        }\n",
       "    },\n",
       "    \"dataZoom\": [\n",
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       "            \"show\": true,\n",
       "            \"type\": \"slider\",\n",
       "            \"realtime\": true,\n",
       "            \"start\": 0,\n",
       "            \"end\": 100,\n",
       "            \"orient\": \"horizontal\",\n",
       "            \"zoomLock\": false,\n",
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       "        {\n",
       "            \"show\": true,\n",
       "            \"type\": \"slider\",\n",
       "            \"realtime\": true,\n",
       "            \"start\": 0,\n",
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       "            \"orient\": \"vertical\",\n",
       "            \"zoomLock\": false,\n",
       "            \"filterMode\": \"filter\"\n",
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       "};\n",
       "                chart_3260ee98df4c40de9b679c4cf0d676a5.setOption(option_3260ee98df4c40de9b679c4cf0d676a5);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x23a22cfc788>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    Line(init_opts=opts.InitOpts(theme=ThemeType.CHALK))\n",
    "    .add_xaxis(liucun_df['日期'].dt.strftime('%Y-%m-%d').to_list())\n",
    "    .add_yaxis('次日留存率', liucun_df['次日留存率'].to_list()) \n",
    "    .add_yaxis('三日留存率', liucun_df['三日留存率'].to_list()) \n",
    "    .add_yaxis('七日留存率', liucun_df['七日留存率'].to_list())\n",
    "    .set_series_opts(\n",
    "        label_opts=opts.LabelOpts(\n",
    "            formatter=JsCode(\"function(x){return Number(x.data[1] * 100).toFixed(2) + '%';}\"), # Labels格式\n",
    "        ),\n",
    "        tooltip_opts=opts.TooltipOpts(\n",
    "            formatter=JsCode(\"function(x){return Number(x.data[1] * 100).toFixed(2) + '%';}\"), # Tooltip格式\n",
    "        )\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"每日用户留存率\"),\n",
    "        yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter=JsCode(\"function(x){return Number(x * 100).toFixed() + '%'}\"))),\n",
    "        toolbox_opts=opts.ToolboxOpts(), # 工具箱\n",
    "        datazoom_opts=[opts.DataZoomOpts(is_realtime=True, range_start=0, range_end=100), \n",
    "                       opts.DataZoomOpts(orient=\"vertical\", range_start=0, range_end=100)], # 同时添加水平和垂直滑动条\n",
    "    )\n",
    "    .render_notebook()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 商品数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 各商品对应的四种用户行为\n",
    "商品行为同样需要确实日期范围，不妨创建1个函数，输入日期范围，默认开始日期为最小值，默认结束日期为今天。\n",
    "这样方便对比2个时间段的数据。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "engine.execute(\"\"\"\n",
    "create or replace function get_behavior_to_item\n",
    "(\n",
    "    start_date1 date default null,\n",
    "    end_date1 date default null\n",
    ")\n",
    "returns table\n",
    "(\n",
    "    \"商品编号\" int,\n",
    "    \"pv\" bigint,\n",
    "    \"fac\" bigint,\n",
    "    \"cart\" bigint,\n",
    "    \"buy\" bigint\n",
    ")\n",
    "as $$\n",
    "    declare start_date2 date;\n",
    "    declare end_date2 date;\n",
    "begin\n",
    "    if start_date1 is null then\n",
    "        select min(event_date) into start_date2 from user_behavior;\n",
    "    else\n",
    "        start_date2 := start_date1;\n",
    "    end if;\n",
    "    if end_date1 is null then\n",
    "        end_date2 := current_date;\n",
    "    else\n",
    "        end_date2 := end_date1;\n",
    "    end if;\n",
    "    if start_date2 > end_date2 then\n",
    "        raise exception '结束日期\"%\"必须大于开始日期\"%\"!', end_date2, start_date2;\n",
    "    end if;\n",
    "    return query select\n",
    "        item_id,\n",
    "        sum(case when behavior_type = 'pv' then 1 else 0 end) as pv,\n",
    "        sum(case when behavior_type = 'fac' then 1 else 0 end),\n",
    "        sum(case when behavior_type = 'cart' then 1 else 0 end),\n",
    "        sum(case when behavior_type = 'buy' then 1 else 0 end) as buy\n",
    "    from user_behavior\n",
    "    where event_date between start_date2 and end_date2\n",
    "    group by item_id\n",
    "    order by buy desc, pv desc;\n",
    "end;\n",
    "$$ language plpgsql;\n",
    "\"\"\")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 25s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>商品编号</th>\n",
       "      <th>pv</th>\n",
       "      <th>fac</th>\n",
       "      <th>cart</th>\n",
       "      <th>buy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3122135</td>\n",
       "      <td>1620</td>\n",
       "      <td>0</td>\n",
       "      <td>339</td>\n",
       "      <td>1395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3031354</td>\n",
       "      <td>15124</td>\n",
       "      <td>0</td>\n",
       "      <td>1589</td>\n",
       "      <td>846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3964583</td>\n",
       "      <td>4412</td>\n",
       "      <td>0</td>\n",
       "      <td>501</td>\n",
       "      <td>597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2560262</td>\n",
       "      <td>8863</td>\n",
       "      <td>0</td>\n",
       "      <td>1100</td>\n",
       "      <td>568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2964774</td>\n",
       "      <td>5688</td>\n",
       "      <td>0</td>\n",
       "      <td>621</td>\n",
       "      <td>511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      商品编号     pv  fac  cart   buy\n",
       "0  3122135   1620    0   339  1395\n",
       "1  3031354  15124    0  1589   846\n",
       "2  3964583   4412    0   501   597\n",
       "3  2560262   8863    0  1100   568\n",
       "4  2964774   5688    0   621   511"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "behavior_to_item_df = pd.read_sql_query(\"\"\"\n",
    "select * from get_behavior_to_item(null, null);\n",
    "\"\"\", con=engine)\n",
    "behavior_to_item_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pv</th>\n",
       "      <th>fac</th>\n",
       "      <th>cart</th>\n",
       "      <th>buy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.950269e+06</td>\n",
       "      <td>3950269.0</td>\n",
       "      <td>3.950269e+06</td>\n",
       "      <td>3.950269e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.959957e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.203856e+00</td>\n",
       "      <td>4.450545e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.067052e+02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.957778e+00</td>\n",
       "      <td>3.395892e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.634900e+04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.589000e+03</td>\n",
       "      <td>1.395000e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 pv        fac          cart           buy\n",
       "count  3.950269e+06  3950269.0  3.950269e+06  3.950269e+06\n",
       "mean   1.959957e+01        0.0  1.203856e+00  4.450545e-01\n",
       "std    1.067052e+02        0.0  6.957778e+00  3.395892e+00\n",
       "min    0.000000e+00        0.0  0.000000e+00  0.000000e+00\n",
       "25%    1.000000e+00        0.0  0.000000e+00  0.000000e+00\n",
       "50%    3.000000e+00        0.0  0.000000e+00  0.000000e+00\n",
       "75%    1.000000e+01        0.0  1.000000e+00  0.000000e+00\n",
       "max    2.634900e+04        0.0  1.589000e+03  1.395000e+03"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behavior_to_item_df[['pv', 'fac', 'cart', 'buy']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 商品复购率\n",
    "单纯的计算商品复购率没有意义，还需要结合商品销量对比。\n",
    "**商品复购率计算如下:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "engine.execute(\"\"\"\n",
    "create or replace function get_repurchase_rate_of_item\n",
    "(\n",
    "    start_date1 date default null,\n",
    "    end_date1 date default null\n",
    ")\n",
    "returns table\n",
    "(\n",
    "    \"商品编号\" int,\n",
    "    \"销售人次\" numeric,\n",
    "    \"复购率\" numeric\n",
    ")\n",
    "as $$\n",
    "    declare start_date2 date;\n",
    "    declare end_date2 date;\n",
    "begin\n",
    "    if start_date1 is null then\n",
    "        select min(event_date) into start_date2 from user_behavior;\n",
    "    else\n",
    "        start_date2 := start_date1;\n",
    "    end if;\n",
    "    if end_date1 is null then\n",
    "        end_date2 := current_date;\n",
    "    else\n",
    "        end_date2 := end_date1;\n",
    "    end if;\n",
    "    if start_date2 > end_date2 then\n",
    "        raise exception '结束日期\"%\"必须大于开始日期\"%\"!', end_date2, start_date2;\n",
    "    end if;\n",
    "    return query select\n",
    "        item_id,\n",
    "        sum(buy_num),\n",
    "        round(\n",
    "            cast(sum(case when buy_num >= 2 then 1 else 0 end) as numeric)/\n",
    "            cast(count(user_id) as numeric)\n",
    "            , 4\n",
    "            ) as repch_rate\n",
    "    from\n",
    "    (\n",
    "        select\n",
    "            user_id,\n",
    "            item_id,\n",
    "            count(*) as buy_num\n",
    "        from user_behavior\n",
    "        where behavior_type = 'buy' and (event_date between start_date2 and end_date2)\n",
    "        group by user_id, item_id\n",
    "    ) ug\n",
    "    group by item_id\n",
    "    order by repch_rate desc;\n",
    "end;\n",
    "$$ language plpgsql;\n",
    "\"\"\")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 20.4 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>商品编号</th>\n",
       "      <th>销售人次</th>\n",
       "      <th>复购率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3209703</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4882259</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4386823</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>491775</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>264954</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      商品编号  销售人次  复购率\n",
       "0  3209703   2.0  1.0\n",
       "1  4882259   2.0  1.0\n",
       "2  4386823   3.0  1.0\n",
       "3   491775   2.0  1.0\n",
       "4   264954   2.0  1.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "item_repurchase_rate_df = pd.read_sql_query(\"\"\"\n",
    "select * from get_repurchase_rate_of_item(null, null);\n",
    "\"\"\", con=engine)\n",
    "item_repurchase_rate_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>商品编号</th>\n",
       "      <th>销售人次</th>\n",
       "      <th>复购率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52701</th>\n",
       "      <td>3122135</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>0.0079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45632</th>\n",
       "      <td>3031354</td>\n",
       "      <td>846.0</td>\n",
       "      <td>0.0766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50859</th>\n",
       "      <td>3964583</td>\n",
       "      <td>597.0</td>\n",
       "      <td>0.0329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49700</th>\n",
       "      <td>2560262</td>\n",
       "      <td>568.0</td>\n",
       "      <td>0.0425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44207</th>\n",
       "      <td>2964774</td>\n",
       "      <td>511.0</td>\n",
       "      <td>0.0851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "          商品编号    销售人次     复购率\n",
       "52701  3122135  1395.0  0.0079\n",
       "45632  3031354   846.0  0.0766\n",
       "50859  3964583   597.0  0.0329\n",
       "49700  2560262   568.0  0.0425\n",
       "44207  2964774   511.0  0.0851"
      ]
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     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_repurchase_rate_df.sort_values(by='销售人次', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>销售人次</th>\n",
       "      <th>复购率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>587200.000000</td>\n",
       "      <td>587200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.994014</td>\n",
       "      <td>0.041850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.363507</td>\n",
       "      <td>0.174634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1395.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "                销售人次            复购率\n",
       "count  587200.000000  587200.000000\n",
       "mean        2.994014       0.041850\n",
       "std         8.363507       0.174634\n",
       "min         1.000000       0.000000\n",
       "25%         1.000000       0.000000\n",
       "50%         1.000000       0.000000\n",
       "75%         2.000000       0.000000\n",
       "max      1395.000000       1.000000"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_repurchase_rate_df[['销售人次', '复购率']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 总结\n",
    "待后续更新"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"color:blue; font-size:200%; font-weight:bold\">参考来源:</p>\n",
    "\n",
    "* [lets-plot.org](https://lets-plot.org/pages/api.html)\n",
    "* [What is Funnel Analysis?](https://data36.com/funnel-analysis/)\n",
    "* [用户行为分析（SQL+Tableau+AARRR模型）](https://zhuanlan.zhihu.com/p/143112660)\n",
    "* [常用的分析方法及模型有哪些？](https://www.zhihu.com/question/20117449)\n",
    "* [用户行为分析：基于Python的AARRR和RFM建模](https://zhuanlan.zhihu.com/p/63383174)\n",
    "* [AARRR模型应用实例（分析淘宝用户行为数据）](https://zhuanlan.zhihu.com/p/285676746)\n",
    "* [用SQL进行电商用户行为分析](https://zhuanlan.zhihu.com/p/158443083)\n",
    "* [淘宝用户行为分析](https://www.heywhale.com/mw/notebook/60249220b23344001584f8f7)<br/>\n",
    "* [淘宝用户行为分析](https://www.heywhale.com/mw/notebook/602f790d891f960015cfc0f8)<br/>\n",
    "* [dask.docs](https://docs.dask.org/en/stable/generated/dask.dataframe.read_csv.html)\n",
    "* [postgresql开窗](https://www.cnblogs.com/funnyzpc/p/9311281.html)\n",
    "* [SQL学习之开窗函数](https://www.cnblogs.com/heisenburg/p/11405181.html)\n",
    "* [joblib并行](https://joblib.readthedocs.io/en/latest/parallel.html)"
   ]
  }
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